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experiment</title> <meta name="description" content="Search results for: microarray experiment"> <meta name="keywords" content="microarray experiment"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button class="d-block 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action="https://publications.waset.org/search"> <div id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="microarray experiment"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 1125</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: microarray experiment</h1> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1125</span> Imputation Technique for Feature Selection in Microarray Data Set</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Younies%20Mahmoud">Younies Mahmoud</a>, <a href="https://publications.waset.org/search?q=Mai%20Mabrouk"> Mai Mabrouk</a>, <a href="https://publications.waset.org/search?q=Elsayed%20Sallam"> Elsayed Sallam</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>Analyzing DNA microarray data sets is a great challenge, which faces the bioinformaticians due to the complication of using statistical and machine learning techniques. The challenge will be doubled if the microarray data sets contain missing data, which happens regularly because these techniques cannot deal with missing data. One of the most important data analysis process on the microarray data set is feature selection. This process finds the most important genes that affect certain disease. In this paper, we introduce a technique for imputing the missing data in microarray data sets while performing feature selection.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=DNA%20microarray" title="DNA microarray">DNA microarray</a>, <a href="https://publications.waset.org/search?q=feature%20selection" title=" feature selection"> feature selection</a>, <a href="https://publications.waset.org/search?q=missing%20data" title=" missing data"> missing data</a>, <a href="https://publications.waset.org/search?q=bioinformatics." title=" bioinformatics."> bioinformatics.</a> </p> <a href="https://publications.waset.org/10000619/imputation-technique-for-feature-selection-in-microarray-data-set" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10000619/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10000619/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10000619/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10000619/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10000619/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10000619/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/10000619/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/10000619/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/10000619/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/10000619/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/10000619.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">2791</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1124</span> Fuzzy Types Clustering for Microarray Data </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Seo%20Young%20Kim">Seo Young Kim</a>, <a href="https://publications.waset.org/search?q=Tai%20Myong%20Choi"> Tai Myong Choi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The main goal of microarray experiments is to quantify the expression of every object on a slide as precisely as possible, with a further goal of clustering the objects. Recently, many studies have discussed clustering issues involving similar patterns of gene expression. This paper presents an application of fuzzy-type methods for clustering DNA microarray data that can be applied to typical comparisons. Clustering and analyses were performed on microarray and simulated data. The results show that fuzzy-possibility c-means clustering substantially improves the findings obtained by others. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Clustering" title="Clustering">Clustering</a>, <a href="https://publications.waset.org/search?q=microarray%20data" title=" microarray data"> microarray data</a>, <a href="https://publications.waset.org/search?q=Fuzzy-type%20clustering" title=" Fuzzy-type clustering"> Fuzzy-type clustering</a>, <a href="https://publications.waset.org/search?q=Validation" title=" Validation"> Validation</a> </p> <a href="https://publications.waset.org/4776/fuzzy-types-clustering-for-microarray-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/4776/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/4776/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/4776/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/4776/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/4776/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/4776/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/4776/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/4776/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/4776/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/4776/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/4776.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1521</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1123</span> Iterative Clustering Algorithm for Analyzing Temporal Patterns of Gene Expression </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Seo%20Young%20Kim">Seo Young Kim</a>, <a href="https://publications.waset.org/search?q=Jae%20Won%20Lee"> Jae Won Lee</a>, <a href="https://publications.waset.org/search?q=Jong%20Sung%20Bae"> Jong Sung Bae</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Microarray experiments are information rich; however, extensive data mining is required to identify the patterns that characterize the underlying mechanisms of action. For biologists, a key aim when analyzing microarray data is to group genes based on the temporal patterns of their expression levels. In this paper, we used an iterative clustering method to find temporal patterns of gene expression. We evaluated the performance of this method by applying it to real sporulation data and simulated data. The patterns obtained using the iterative clustering were found to be superior to those obtained using existing clustering algorithms. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Clustering" title="Clustering">Clustering</a>, <a href="https://publications.waset.org/search?q=microarray%20experiment" title=" microarray experiment"> microarray experiment</a>, <a href="https://publications.waset.org/search?q=temporal%0Apattern%20of%20gene%20expression%20data." title=" temporal pattern of gene expression data."> temporal pattern of gene expression data.</a> </p> <a href="https://publications.waset.org/6802/iterative-clustering-algorithm-for-analyzing-temporal-patterns-of-gene-expression" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/6802/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/6802/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/6802/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/6802/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/6802/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/6802/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/6802/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/6802/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/6802/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/6802/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/6802.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1355</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1122</span> A Novel Microarray Biclustering Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Chieh-Yuan%20Tsai">Chieh-Yuan Tsai</a>, <a href="https://publications.waset.org/search?q=Chuang-Cheng%20Chiu"> Chuang-Cheng Chiu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Biclustering aims at identifying several biclusters that reveal potential local patterns from a microarray matrix. A bicluster is a sub-matrix of the microarray consisting of only a subset of genes co-regulates in a subset of conditions. In this study, we extend the motif of subspace clustering to present a K-biclusters clustering (KBC) algorithm for the microarray biclustering issue. Besides minimizing the dissimilarities between genes and bicluster centers within all biclusters, the objective function of the KBC algorithm additionally takes into account how to minimize the residues within all biclusters based on the mean square residue model. In addition, the objective function also maximizes the entropy of conditions to stimulate more conditions to contribute the identification of biclusters. The KBC algorithm adopts the K-means type clustering process to efficiently make the partition of K biclusters be optimized. A set of experiments on a practical microarray dataset are demonstrated to show the performance of the proposed KBC algorithm. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Microarray" title="Microarray">Microarray</a>, <a href="https://publications.waset.org/search?q=Biclustering" title=" Biclustering"> Biclustering</a>, <a href="https://publications.waset.org/search?q=Subspace%20clustering" title=" Subspace clustering"> Subspace clustering</a>, <a href="https://publications.waset.org/search?q=Meansquare%20residue%20model." title=" Meansquare residue model."> Meansquare residue model.</a> </p> <a href="https://publications.waset.org/9470/a-novel-microarray-biclustering-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/9470/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/9470/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/9470/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/9470/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/9470/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/9470/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/9470/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/9470/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/9470/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/9470/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/9470.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1615</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1121</span> Principal Component Analysis using Singular Value Decomposition of Microarray Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Dong%20Hoon%20Lim">Dong Hoon Lim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>A series of microarray experiments produces observations of differential expression for thousands of genes across multiple conditions. Principal component analysis(PCA) has been widely used in multivariate data analysis to reduce the dimensionality of the data in order to simplify subsequent analysis and allow for summarization of the data in a parsimonious manner. PCA, which can be implemented via a singular value decomposition(SVD), is useful for analysis of microarray data. For application of PCA using SVD we use the DNA microarray data for the small round blue cell tumors(SRBCT) of childhood by Khan et al.(2001). To decide the number of components which account for sufficient amount of information we draw scree plot. Biplot, a graphic display associated with PCA, reveals important features that exhibit relationship between variables and also the relationship of variables with observations.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Principal%20component%20analysis" title="Principal component analysis">Principal component analysis</a>, <a href="https://publications.waset.org/search?q=singular%20value%20decomposition" title=" singular value decomposition"> singular value decomposition</a>, <a href="https://publications.waset.org/search?q=microarray%20data" title=" microarray data"> microarray data</a>, <a href="https://publications.waset.org/search?q=SRBCT" title=" SRBCT"> SRBCT</a> </p> <a href="https://publications.waset.org/16593/principal-component-analysis-using-singular-value-decomposition-of-microarray-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/16593/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/16593/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/16593/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/16593/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/16593/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/16593/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/16593/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/16593/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/16593/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/16593/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/16593.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">3250</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1120</span> An Automatic Gridding and Contour Based Segmentation Approach Applied to DNA Microarray Image Analysis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Alexandra%20Oliveros">Alexandra Oliveros</a>, <a href="https://publications.waset.org/search?q=Miguel%20Sotaquir%C3%A1"> Miguel Sotaquir谩</a> </p> <p class="card-text"><strong>Abstract:</strong></p> DNA microarray technology is widely used by geneticists to diagnose or treat diseases through gene expression. This technology is based on the hybridization of a tissue-s DNA sequence into a substrate and the further analysis of the image formed by the thousands of genes in the DNA as green, red or yellow spots. The process of DNA microarray image analysis involves finding the location of the spots and the quantification of the expression level of these. In this paper, a tool to perform DNA microarray image analysis is presented, including a spot addressing method based on the image projections, the spot segmentation through contour based segmentation and the extraction of relevant information due to gene expression. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Contour%20segmentation" title="Contour segmentation">Contour segmentation</a>, <a href="https://publications.waset.org/search?q=DNA%20microarrays" title=" DNA microarrays"> DNA microarrays</a>, <a href="https://publications.waset.org/search?q=edge%0Adetection" title=" edge detection"> edge detection</a>, <a href="https://publications.waset.org/search?q=image%20processing" title=" image processing"> image processing</a>, <a href="https://publications.waset.org/search?q=segmentation" title=" segmentation"> segmentation</a>, <a href="https://publications.waset.org/search?q=spot%20addressing." title=" spot addressing."> spot addressing.</a> </p> <a href="https://publications.waset.org/13665/an-automatic-gridding-and-contour-based-segmentation-approach-applied-to-dna-microarray-image-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/13665/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/13665/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/13665/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/13665/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/13665/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/13665/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/13665/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/13665/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/13665/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/13665/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/13665.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1390</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1119</span> Probe Selection for Pathway-Specific Microarray Probe Design Minimizing Melting Temperature Variance</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Fabian%20Horn">Fabian Horn</a>, <a href="https://publications.waset.org/search?q=Reinhard%20Guthke"> Reinhard Guthke</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>In molecular biology, microarray technology is widely and successfully utilized to efficiently measure gene activity. If working with less studied organisms, methods to design custom-made microarray probes are available. One design criterion is to select probes with minimal melting temperature variances thus ensuring similar hybridization properties. If the microarray application focuses on the investigation of metabolic pathways, it is not necessary to cover the whole genome. It is more efficient to cover each metabolic pathway with a limited number of genes. Firstly, an approach is presented which minimizes the overall melting temperature variance of selected probes for all genes of interest. Secondly, the approach is extended to include the additional constraints of covering all pathways with a limited number of genes while minimizing the overall variance. The new optimization problem is solved by a bottom-up programming approach which reduces the complexity to make it computationally feasible. The new method is exemplary applied for the selection of microarray probes in order to cover all fungal secondary metabolite gene clusters for Aspergillus terreus.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=bottom-up%20approach" title="bottom-up approach">bottom-up approach</a>, <a href="https://publications.waset.org/search?q=gene%20clusters" title=" gene clusters"> gene clusters</a>, <a href="https://publications.waset.org/search?q=melting%20temperature" title=" melting temperature"> melting temperature</a>, <a href="https://publications.waset.org/search?q=metabolic%20pathway" title=" metabolic pathway"> metabolic pathway</a>, <a href="https://publications.waset.org/search?q=microarray%20probe%20design" title=" microarray probe design"> microarray probe design</a>, <a href="https://publications.waset.org/search?q=probe%20selection" title=" probe selection"> probe selection</a> </p> <a href="https://publications.waset.org/4421/probe-selection-for-pathway-specific-microarray-probe-design-minimizing-melting-temperature-variance" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/4421/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/4421/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/4421/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/4421/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/4421/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/4421/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/4421/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/4421/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/4421/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/4421/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/4421.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1559</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1118</span> Influence of Noise on the Inference of Dynamic Bayesian Networks from Short Time Series</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Frank%20Emmert%20Streib">Frank Emmert Streib</a>, <a href="https://publications.waset.org/search?q=Matthias%20Dehmer"> Matthias Dehmer</a>, <a href="https://publications.waset.org/search?q=G%C3%B6khan%20H.%20Bak%C4%B1r"> G枚khan H. Bak谋r</a>, <a href="https://publications.waset.org/search?q=Max%20M%C3%BChlhauser"> Max M眉hlhauser</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper we investigate the influence of external noise on the inference of network structures. The purpose of our simulations is to gain insights in the experimental design of microarray experiments to infer, e.g., transcription regulatory networks from microarray experiments. Here external noise means, that the dynamics of the system under investigation, e.g., temporal changes of mRNA concentration, is affected by measurement errors. Additionally to external noise another problem occurs in the context of microarray experiments. Practically, it is not possible to monitor the mRNA concentration over an arbitrary long time period as demanded by the statistical methods used to learn the underlying network structure. For this reason, we use only short time series to make our simulations more biologically plausible. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Dynamic%20Bayesian%20networks" title="Dynamic Bayesian networks">Dynamic Bayesian networks</a>, <a href="https://publications.waset.org/search?q=structure%20learning" title=" structure learning"> structure learning</a>, <a href="https://publications.waset.org/search?q=gene%20networks" title=" gene networks"> gene networks</a>, <a href="https://publications.waset.org/search?q=Markov%20chain%20Monte%20Carlo" title=" Markov chain Monte Carlo"> Markov chain Monte Carlo</a>, <a href="https://publications.waset.org/search?q=microarray%20data." title=" microarray data."> microarray data.</a> </p> <a href="https://publications.waset.org/6825/influence-of-noise-on-the-inference-of-dynamic-bayesian-networks-from-short-time-series" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/6825/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/6825/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/6825/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/6825/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/6825/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/6825/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/6825/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/6825/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/6825/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/6825/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/6825.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1611</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1117</span> A Cuckoo Search with Differential Evolution for Clustering Microarray Gene Expression Data </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=M.%20Pandi">M. Pandi</a>, <a href="https://publications.waset.org/search?q=K.%20Premalatha"> K. Premalatha</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>A DNA microarray technology is a collection of microscopic DNA spots attached to a solid surface. Scientists use DNA microarrays to measure the expression levels of large numbers of genes simultaneously or to genotype multiple regions of a genome. Elucidating the patterns hidden in gene expression data offers a tremendous opportunity for an enhanced understanding of functional genomics. However, the large number of genes and the complexity of biological networks greatly increase the challenges of comprehending and interpreting the resulting mass of data, which often consists of millions of measurements. It is handled by clustering which reveals the natural structures and identifying the interesting patterns in the underlying data. In this paper, gene based clustering in gene expression data is proposed using Cuckoo Search with Differential Evolution (CS-DE). The experiment results are analyzed with gene expression benchmark datasets. The results show that CS-DE outperforms CS in benchmark datasets. To find the validation of the clustering results, this work is tested with one internal and one external cluster validation indexes.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=DNA" title="DNA">DNA</a>, <a href="https://publications.waset.org/search?q=Microarray" title=" Microarray"> Microarray</a>, <a href="https://publications.waset.org/search?q=genomics" title=" genomics"> genomics</a>, <a href="https://publications.waset.org/search?q=Cuckoo%20Search" title=" Cuckoo Search"> Cuckoo Search</a>, <a href="https://publications.waset.org/search?q=Differential%20Evolution" title=" Differential Evolution"> Differential Evolution</a>, <a href="https://publications.waset.org/search?q=Gene%20expression%20data" title=" Gene expression data"> Gene expression data</a>, <a href="https://publications.waset.org/search?q=Clustering." title=" Clustering."> Clustering.</a> </p> <a href="https://publications.waset.org/10003899/a-cuckoo-search-with-differential-evolution-for-clustering-microarray-gene-expression-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10003899/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10003899/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10003899/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10003899/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10003899/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10003899/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/10003899/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/10003899/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/10003899/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/10003899/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/10003899.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1483</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1116</span> Integration of Microarray Data into a Genome-Scale Metabolic Model to Study Flux Distribution after Gene Knockout</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Mona%20Heydari">Mona Heydari</a>, <a href="https://publications.waset.org/search?q=Ehsan%20Motamedian"> Ehsan Motamedian</a>, <a href="https://publications.waset.org/search?q=Seyed%20Abbas%20Shojaosadati"> Seyed Abbas Shojaosadati</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Prediction of perturbations after genetic manipulation (especially gene knockout) is one of the important challenges in systems biology. In this paper, a new algorithm is introduced that integrates microarray data into the metabolic model. The algorithm was used to study the change in the cell phenotype after knockout of Gss gene in Escherichia coli BW25113. Algorithm implementation indicated that gene deletion resulted in more activation of the metabolic network. Growth yield was more and less regulating gene were identified for mutant in comparison with the wild-type strain. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Metabolic%20network" title="Metabolic network">Metabolic network</a>, <a href="https://publications.waset.org/search?q=gene%20knockout" title=" gene knockout"> gene knockout</a>, <a href="https://publications.waset.org/search?q=flux%20balance%0D%0Aanalysis" title=" flux balance analysis"> flux balance analysis</a>, <a href="https://publications.waset.org/search?q=microarray%20data" title=" microarray data"> microarray data</a>, <a href="https://publications.waset.org/search?q=integration." title=" integration."> integration.</a> </p> <a href="https://publications.waset.org/10004894/integration-of-microarray-data-into-a-genome-scale-metabolic-model-to-study-flux-distribution-after-gene-knockout" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10004894/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10004894/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10004894/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10004894/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10004894/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10004894/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/10004894/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/10004894/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/10004894/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/10004894/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/10004894.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">996</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1115</span> A Systems Approach to Gene Ranking from DNA Microarray Data of Cervical Cancer</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Frank%20Emmert%20Streib">Frank Emmert Streib</a>, <a href="https://publications.waset.org/search?q=Matthias%20Dehmer"> Matthias Dehmer</a>, <a href="https://publications.waset.org/search?q=Jing%20Liu"> Jing Liu</a>, <a href="https://publications.waset.org/search?q=Max%20M%C3%BChlhauser"> Max M眉hlhauser</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper we present a method for gene ranking from DNA microarray data. More precisely, we calculate the correlation networks, which are unweighted and undirected graphs, from microarray data of cervical cancer whereas each network represents a tissue of a certain tumor stage and each node in the network represents a gene. From these networks we extract one tree for each gene by a local decomposition of the correlation network. The interpretation of a tree is that it represents the n-nearest neighbor genes on the n-th level of a tree, measured by the Dijkstra distance, and, hence, gives the local embedding of a gene within the correlation network. For the obtained trees we measure the pairwise similarity between trees rooted by the same gene from normal to cancerous tissues. This evaluates the modification of the tree topology due to progression of the tumor. Finally, we rank the obtained similarity values from all tissue comparisons and select the top ranked genes. For these genes the local neighborhood in the correlation networks changes most between normal and cancerous tissues. As a result we find that the top ranked genes are candidates suspected to be involved in tumor growth and, hence, indicates that our method captures essential information from the underlying DNA microarray data of cervical cancer. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Graph%20similarity" title="Graph similarity">Graph similarity</a>, <a href="https://publications.waset.org/search?q=DNA%20microarray%20data" title=" DNA microarray data"> DNA microarray data</a>, <a href="https://publications.waset.org/search?q=cancer." title=" cancer."> cancer.</a> </p> <a href="https://publications.waset.org/436/a-systems-approach-to-gene-ranking-from-dna-microarray-data-of-cervical-cancer" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/436/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/436/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/436/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/436/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/436/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/436/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/436/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/436/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/436/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/436/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/436.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1756</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1114</span> Ranking Genes from DNA Microarray Data of Cervical Cancer by a local Tree Comparison</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Frank%20Emmert-Streib">Frank Emmert-Streib</a>, <a href="https://publications.waset.org/search?q=Matthias%20Dehmer"> Matthias Dehmer</a>, <a href="https://publications.waset.org/search?q=Jing%20Liu"> Jing Liu</a>, <a href="https://publications.waset.org/search?q=Max%20Muhlhauser"> Max Muhlhauser</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>The major objective of this paper is to introduce a new method to select genes from DNA microarray data. As criterion to select genes we suggest to measure the local changes in the correlation graph of each gene and to select those genes whose local changes are largest. More precisely, we calculate the correlation networks from DNA microarray data of cervical cancer whereas each network represents a tissue of a certain tumor stage and each node in the network represents a gene. From these networks we extract one tree for each gene by a local decomposition of the correlation network. The interpretation of a tree is that it represents the n-nearest neighbor genes on the n-th level of a tree, measured by the Dijkstra distance, and, hence, gives the local embedding of a gene within the correlation network. For the obtained trees we measure the pairwise similarity between trees rooted by the same gene from normal to cancerous tissues. This evaluates the modification of the tree topology due to tumor progression. Finally, we rank the obtained similarity values from all tissue comparisons and select the top ranked genes. For these genes the local neighborhood in the correlation networks changes most between normal and cancerous tissues. As a result we find that the top ranked genes are candidates suspected to be involved in tumor growth. This indicates that our method captures essential information from the underlying DNA microarray data of cervical cancer.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Graph%20similarity" title="Graph similarity">Graph similarity</a>, <a href="https://publications.waset.org/search?q=generalized%20trees" title=" generalized trees"> generalized trees</a>, <a href="https://publications.waset.org/search?q=graph%20alignment" title=" graph alignment"> graph alignment</a>, <a href="https://publications.waset.org/search?q=DNA%20microarray%20data" title=" DNA microarray data"> DNA microarray data</a>, <a href="https://publications.waset.org/search?q=cervical%20cancer." title=" cervical cancer."> cervical cancer.</a> </p> <a href="https://publications.waset.org/4875/ranking-genes-from-dna-microarray-data-of-cervical-cancer-by-a-local-tree-comparison" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/4875/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/4875/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/4875/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/4875/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/4875/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/4875/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/4875/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/4875/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/4875/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/4875/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/4875.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1753</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1113</span> Systholic Boolean Orthonormalizer Network in Wavelet Domain for Microarray Denoising</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Mario%20Mastriani">Mario Mastriani</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>We describe a novel method for removing noise (in wavelet domain) of unknown variance from microarrays. The method is based on the following procedure: We apply 1) Bidimentional Discrete Wavelet Transform (DWT-2D) to the Noisy Microarray, 2) scaling and rounding to the coefficients of the highest subbands (to obtain integer and positive coefficients), 3) bit-slicing to the new highest subbands (to obtain bit-planes), 4) then we apply the Systholic Boolean Orthonormalizer Network (SBON) to the input bit-plane set and we obtain two orthonormal otput bit-plane sets (in a Boolean sense), we project a set on the other one, by means of an AND operation, and then, 5) we apply re-assembling, and, 6) rescaling. Finally, 7) we apply Inverse DWT-2D and reconstruct a microarray from the modified wavelet coefficients. Denoising results compare favorably to the most of methods in use at the moment.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Bit-Plane" title="Bit-Plane">Bit-Plane</a>, <a href="https://publications.waset.org/search?q=Boolean%20Orthonormalization%20Process" title=" Boolean Orthonormalization Process"> Boolean Orthonormalization Process</a>, <a href="https://publications.waset.org/search?q=Denoising" title=" Denoising"> Denoising</a>, <a href="https://publications.waset.org/search?q=Microarrays" title=" Microarrays"> Microarrays</a>, <a href="https://publications.waset.org/search?q=Wavelets" title=" Wavelets"> Wavelets</a> </p> <a href="https://publications.waset.org/9632/systholic-boolean-orthonormalizer-network-in-wavelet-domain-for-microarray-denoising" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/9632/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/9632/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/9632/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/9632/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/9632/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/9632/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/9632/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/9632/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/9632/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/9632/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/9632.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1490</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1112</span> A Hybrid Approach for Selection of Relevant Features for Microarray Datasets</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=R.%20K.%20Agrawal">R. K. Agrawal</a>, <a href="https://publications.waset.org/search?q=Rajni%20Bala"> Rajni Bala</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>Developing an accurate classifier for high dimensional microarray datasets is a challenging task due to availability of small sample size. Therefore, it is important to determine a set of relevant genes that classify the data well. Traditionally, gene selection method often selects the top ranked genes according to their discriminatory power. Often these genes are correlated with each other resulting in redundancy. In this paper, we have proposed a hybrid method using feature ranking and wrapper method (Genetic Algorithm with multiclass SVM) to identify a set of relevant genes that classify the data more accurately. A new fitness function for genetic algorithm is defined that focuses on selecting the smallest set of genes that provides maximum accuracy. Experiments have been carried on four well-known datasets1. The proposed method provides better results in comparison to the results found in the literature in terms of both classification accuracy and number of genes selected.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Gene%20selection" title="Gene selection">Gene selection</a>, <a href="https://publications.waset.org/search?q=genetic%20algorithm" title=" genetic algorithm"> genetic algorithm</a>, <a href="https://publications.waset.org/search?q=microarray%20datasets" title=" microarray datasets"> microarray datasets</a>, <a href="https://publications.waset.org/search?q=multi-class%20SVM." title=" multi-class SVM."> multi-class SVM.</a> </p> <a href="https://publications.waset.org/8658/a-hybrid-approach-for-selection-of-relevant-features-for-microarray-datasets" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/8658/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/8658/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/8658/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/8658/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/8658/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/8658/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/8658/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/8658/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/8658/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/8658/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/8658.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">2058</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1111</span> An SVM based Classification Method for Cancer Data using Minimum Microarray Gene Expressions</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=R.%20Mallika">R. Mallika</a>, <a href="https://publications.waset.org/search?q=V.%20Saravanan"> V. Saravanan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper gives a novel method for improving classification performance for cancer classification with very few microarray Gene expression data. The method employs classification with individual gene ranking and gene subset ranking. For selection and classification, the proposed method uses the same classifier. The method is applied to three publicly available cancer gene expression datasets from Lymphoma, Liver and Leukaemia datasets. Three different classifiers namely Support vector machines-one against all (SVM-OAA), K nearest neighbour (KNN) and Linear Discriminant analysis (LDA) were tested and the results indicate the improvement in performance of SVM-OAA classifier with satisfactory results on all the three datasets when compared with the other two classifiers. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Support%20vector%20machines-one%20against%20all" title="Support vector machines-one against all">Support vector machines-one against all</a>, <a href="https://publications.waset.org/search?q=cancerclassification" title=" cancerclassification"> cancerclassification</a>, <a href="https://publications.waset.org/search?q=Linear%20Discriminant%20analysis" title=" Linear Discriminant analysis"> Linear Discriminant analysis</a>, <a href="https://publications.waset.org/search?q=K%20nearest%20neighbour" title=" K nearest neighbour"> K nearest neighbour</a>, <a href="https://publications.waset.org/search?q=microarray%20gene%20expression" title="microarray gene expression">microarray gene expression</a>, <a href="https://publications.waset.org/search?q=gene%20pair%20ranking." title=" gene pair ranking."> gene pair ranking.</a> </p> <a href="https://publications.waset.org/3742/an-svm-based-classification-method-for-cancer-data-using-minimum-microarray-gene-expressions" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/3742/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/3742/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/3742/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/3742/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/3742/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/3742/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/3742/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/3742/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/3742/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/3742/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/3742.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">2562</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1110</span> Performance Analysis of Genetic Algorithm with kNN and SVM for Feature Selection in Tumor Classification</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=C.%20Gunavathi">C. Gunavathi</a>, <a href="https://publications.waset.org/search?q=K.%20Premalatha"> K. Premalatha</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>Tumor classification is a key area of research in the field of bioinformatics. Microarray technology is commonly used in the study of disease diagnosis using gene expression levels. The main drawback of gene expression data is that it contains thousands of genes and a very few samples. Feature selection methods are used to select the informative genes from the microarray. These methods considerably improve the classification accuracy. In the proposed method, Genetic Algorithm (GA) is used for effective feature selection. Informative genes are identified based on the T-Statistics, Signal-to-Noise Ratio (SNR) and F-Test values. The initial candidate solutions of GA are obtained from top-m informative genes. The classification accuracy of k-Nearest Neighbor (kNN) method is used as the fitness function for GA. In this work, kNN and Support Vector Machine (SVM) are used as the classifiers. The experimental results show that the proposed work is suitable for effective feature selection. With the help of the selected genes, GA-kNN method achieves 100% accuracy in 4 datasets and GA-SVM method achieves in 5 out of 10 datasets. The GA with kNN and SVM methods are demonstrated to be an accurate method for microarray based tumor classification.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=F-Test" title="F-Test">F-Test</a>, <a href="https://publications.waset.org/search?q=Gene%20Expression" title=" Gene Expression"> Gene Expression</a>, <a href="https://publications.waset.org/search?q=Genetic%20Algorithm" title=" Genetic Algorithm"> Genetic Algorithm</a>, <a href="https://publications.waset.org/search?q=k-%0D%0ANearest-Neighbor" title=" k- Nearest-Neighbor"> k- Nearest-Neighbor</a>, <a href="https://publications.waset.org/search?q=Microarray" title=" Microarray"> Microarray</a>, <a href="https://publications.waset.org/search?q=Signal-to-Noise%20Ratio" title=" Signal-to-Noise Ratio"> Signal-to-Noise Ratio</a>, <a href="https://publications.waset.org/search?q=Support%0D%0AVector%20Machine" title=" Support Vector Machine"> Support Vector Machine</a>, <a href="https://publications.waset.org/search?q=T-statistics" title=" T-statistics"> T-statistics</a>, <a href="https://publications.waset.org/search?q=Tumor%20Classification." title=" Tumor Classification."> Tumor Classification.</a> </p> <a href="https://publications.waset.org/9999410/performance-analysis-of-genetic-algorithm-with-knn-and-svm-for-feature-selection-in-tumor-classification" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/9999410/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/9999410/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/9999410/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/9999410/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/9999410/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/9999410/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/9999410/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/9999410/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/9999410/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/9999410/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/9999410.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">4538</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1109</span> Comparative Study on Swarm Intelligence Techniques for Biclustering of Microarray Gene Expression Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=R.%20Balamurugan">R. Balamurugan</a>, <a href="https://publications.waset.org/search?q=A.%20M.%20Natarajan"> A. M. Natarajan</a>, <a href="https://publications.waset.org/search?q=K.%20Premalatha"> K. Premalatha</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>Microarray gene expression data play a vital in biological processes, gene regulation and disease mechanism. Biclustering in gene expression data is a subset of the genes indicating consistent patterns under the subset of the conditions. Finding a biclustering is an optimization problem. In recent years, swarm intelligence techniques are popular due to the fact that many real-world problems are increasingly large, complex and dynamic. By reasons of the size and complexity of the problems, it is necessary to find an optimization technique whose efficiency is measured by finding the near optimal solution within a reasonable amount of time. In this paper, the algorithmic concepts of the Particle Swarm Optimization (PSO), Shuffled Frog Leaping (SFL) and Cuckoo Search (CS) algorithms have been analyzed for the four benchmark gene expression dataset. The experiment results show that CS outperforms PSO and SFL for 3 datasets and SFL give better performance in one dataset. Also this work determines the biological relevance of the biclusters with Gene Ontology in terms of function, process and component.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Particle%20swarm%20optimization" title="Particle swarm optimization">Particle swarm optimization</a>, <a href="https://publications.waset.org/search?q=Shuffled%20frog%20leaping" title=" Shuffled frog leaping"> Shuffled frog leaping</a>, <a href="https://publications.waset.org/search?q=Cuckoo%20search" title=" Cuckoo search"> Cuckoo search</a>, <a href="https://publications.waset.org/search?q=biclustering" title=" biclustering"> biclustering</a>, <a href="https://publications.waset.org/search?q=gene%20expression%20data." title=" gene expression data."> gene expression data.</a> </p> <a href="https://publications.waset.org/9997844/comparative-study-on-swarm-intelligence-techniques-for-biclustering-of-microarray-gene-expression-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/9997844/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/9997844/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/9997844/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/9997844/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/9997844/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/9997844/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/9997844/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/9997844/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/9997844/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/9997844/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/9997844.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">2663</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1108</span> Microarrays Denoising via Smoothing of Coefficients in Wavelet Domain </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Mario%20Mastriani">Mario Mastriani</a>, <a href="https://publications.waset.org/search?q=Alberto%20E.%20Giraldez"> Alberto E. Giraldez</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>We describe a novel method for removing noise (in wavelet domain) of unknown variance from microarrays. The method is based on a smoothing of the coefficients of the highest subbands. Specifically, we decompose the noisy microarray into wavelet subbands, apply smoothing within each highest subband, and reconstruct a microarray from the modified wavelet coefficients. This process is applied a single time, and exclusively to the first level of decomposition, i.e., in most of the cases, it is not necessary a multirresoltuion analysis. Denoising results compare favorably to the most of methods in use at the moment.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Directional%20smoothing" title="Directional smoothing">Directional smoothing</a>, <a href="https://publications.waset.org/search?q=denoising" title=" denoising"> denoising</a>, <a href="https://publications.waset.org/search?q=edge%20preservation" title=" edge preservation"> edge preservation</a>, <a href="https://publications.waset.org/search?q=microarrays" title="microarrays">microarrays</a>, <a href="https://publications.waset.org/search?q=thresholding" title=" thresholding"> thresholding</a>, <a href="https://publications.waset.org/search?q=wavelets" title=" wavelets"> wavelets</a> </p> <a href="https://publications.waset.org/9224/microarrays-denoising-via-smoothing-of-coefficients-in-wavelet-domain" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/9224/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/9224/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/9224/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/9224/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/9224/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/9224/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/9224/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/9224/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/9224/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/9224/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/9224.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1502</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1107</span> Dimension Reduction of Microarray Data Based on Local Principal Component</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Ali%20Anaissi">Ali Anaissi</a>, <a href="https://publications.waset.org/search?q=Paul%20J.%20Kennedy"> Paul J. Kennedy</a>, <a href="https://publications.waset.org/search?q=Madhu%20Goyal"> Madhu Goyal</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>Analysis and visualization of microarraydata is veryassistantfor biologists and clinicians in the field of diagnosis and treatment of patients. It allows Clinicians to better understand the structure of microarray and facilitates understanding gene expression in cells. However, microarray dataset is a complex data set and has thousands of features and a very small number of observations. This very high dimensional data set often contains some noise, non-useful information and a small number of relevant features for disease or genotype. This paper proposes a non-linear dimensionality reduction algorithm Local Principal Component (LPC) which aims to maps high dimensional data to a lower dimensional space. The reduced data represents the most important variables underlying the original data. Experimental results and comparisons are presented to show the quality of the proposed algorithm. Moreover, experiments also show how this algorithm reduces high dimensional data whilst preserving the neighbourhoods of the points in the low dimensional space as in the high dimensional space.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Linear%20Dimension%20Reduction" title="Linear Dimension Reduction">Linear Dimension Reduction</a>, <a href="https://publications.waset.org/search?q=Non-Linear%20Dimension%0D%0AReduction" title=" Non-Linear Dimension Reduction"> Non-Linear Dimension Reduction</a>, <a href="https://publications.waset.org/search?q=Principal%20Component%20Analysis" title=" Principal Component Analysis"> Principal Component Analysis</a>, <a href="https://publications.waset.org/search?q=Biologists." title=" Biologists."> Biologists.</a> </p> <a href="https://publications.waset.org/13857/dimension-reduction-of-microarray-data-based-on-local-principal-component" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/13857/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/13857/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/13857/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/13857/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/13857/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/13857/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/13857/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/13857/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/13857/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/13857/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/13857.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1574</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1106</span> A Comparison of SVM-based Criteria in Evolutionary Method for Gene Selection and Classification of Microarray Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Rameswar%20Debnath">Rameswar Debnath</a>, <a href="https://publications.waset.org/search?q=Haruhisa%20Takahashi"> Haruhisa Takahashi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> An evolutionary method whose selection and recombination operations are based on generalization error-bounds of support vector machine (SVM) can select a subset of potentially informative genes for SVM classifier very efficiently [7]. In this paper, we will use the derivative of error-bound (first-order criteria) to select and recombine gene features in the evolutionary process, and compare the performance of the derivative of error-bound with the error-bound itself (zero-order) in the evolutionary process. We also investigate several error-bounds and their derivatives to compare the performance, and find the best criteria for gene selection and classification. We use 7 cancer-related human gene expression datasets to evaluate the performance of the zero-order and first-order criteria of error-bounds. Though both criteria have the same strategy in theoretically, experimental results demonstrate the best criterion for microarray gene expression data. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=support%20vector%20machine" title="support vector machine">support vector machine</a>, <a href="https://publications.waset.org/search?q=generalization%20error-bound" title=" generalization error-bound"> generalization error-bound</a>, <a href="https://publications.waset.org/search?q=feature%20selection" title="feature selection">feature selection</a>, <a href="https://publications.waset.org/search?q=evolutionary%20algorithm" title=" evolutionary algorithm"> evolutionary algorithm</a>, <a href="https://publications.waset.org/search?q=microarray%20data" title=" microarray data"> microarray data</a> </p> <a href="https://publications.waset.org/10270/a-comparison-of-svm-based-criteria-in-evolutionary-method-for-gene-selection-and-classification-of-microarray-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10270/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10270/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10270/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10270/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10270/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10270/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/10270/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/10270/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/10270/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/10270/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/10270.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1536</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1105</span> Novel Hybrid Method for Gene Selection and Cancer Prediction</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Liping%20Jing">Liping Jing</a>, <a href="https://publications.waset.org/search?q=Michael%20K.%20Ng"> Michael K. Ng</a>, <a href="https://publications.waset.org/search?q=Tieyong%20Zeng"> Tieyong Zeng</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Microarray data profiles gene expression on a whole genome scale, therefore, it provides a good way to study associations between gene expression and occurrence or progression of cancer. More and more researchers realized that microarray data is helpful to predict cancer sample. However, the high dimension of gene expressions is much larger than the sample size, which makes this task very difficult. Therefore, how to identify the significant genes causing cancer becomes emergency and also a hot and hard research topic. Many feature selection algorithms have been proposed in the past focusing on improving cancer predictive accuracy at the expense of ignoring the correlations between the features. In this work, a novel framework (named by SGS) is presented for stable gene selection and efficient cancer prediction . The proposed framework first performs clustering algorithm to find the gene groups where genes in each group have higher correlation coefficient, and then selects the significant genes in each group with Bayesian Lasso and important gene groups with group Lasso, and finally builds prediction model based on the shrinkage gene space with efficient classification algorithm (such as, SVM, 1NN, Regression and etc.). Experiment results on real world data show that the proposed framework often outperforms the existing feature selection and prediction methods, say SAM, IG and Lasso-type prediction model. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Gene%20Selection" title="Gene Selection">Gene Selection</a>, <a href="https://publications.waset.org/search?q=Cancer%20Prediction" title=" Cancer Prediction"> Cancer Prediction</a>, <a href="https://publications.waset.org/search?q=Lasso" title=" Lasso"> Lasso</a>, <a href="https://publications.waset.org/search?q=Clustering" title=" Clustering"> Clustering</a>, <a href="https://publications.waset.org/search?q=Classification." title="Classification.">Classification.</a> </p> <a href="https://publications.waset.org/8670/novel-hybrid-method-for-gene-selection-and-cancer-prediction" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/8670/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/8670/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/8670/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/8670/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/8670/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/8670/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/8670/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/8670/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/8670/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/8670/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/8670.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">2044</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1104</span> Categorization and Estimation of Relative Connectivity of Genes from Meta-OFTEN Network</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=U.%20Kairov">U. Kairov</a>, <a href="https://publications.waset.org/search?q=T.%20Karpenyuk"> T. Karpenyuk</a>, <a href="https://publications.waset.org/search?q=E.%20Ramanculov"> E. Ramanculov</a>, <a href="https://publications.waset.org/search?q=A.%20Zinovyev"> A. Zinovyev</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The most common result of analysis of highthroughput data in molecular biology represents a global list of genes, ranked accordingly to a certain score. The score can be a measure of differential expression. Recent work proposed a new method for selecting a number of genes in a ranked gene list from microarray gene expression data such that this set forms the Optimally Functionally Enriched Network (OFTEN), formed by known physical interactions between genes or their products. Here we present calculation results of relative connectivity of genes from META-OFTEN network and tentative biological interpretation of the most reproducible signal. The relative connectivity and inbetweenness values of genes from META-OFTEN network were estimated. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Microarray" title="Microarray">Microarray</a>, <a href="https://publications.waset.org/search?q=META-OFTEN" title=" META-OFTEN"> META-OFTEN</a>, <a href="https://publications.waset.org/search?q=gene%20network." title=" gene network."> gene network.</a> </p> <a href="https://publications.waset.org/307/categorization-and-estimation-of-relative-connectivity-of-genes-from-meta-often-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/307/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/307/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/307/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/307/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/307/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/307/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/307/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/307/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/307/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/307/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/307.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1627</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1103</span> Statistical Measures and Optimization Algorithms for Gene Selection in Lung and Ovarian Tumor</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=C.%20Gunavathi">C. Gunavathi</a>, <a href="https://publications.waset.org/search?q=K.%20Premalatha"> K. Premalatha</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>Microarray technology is universally used in the study of disease diagnosis using gene expression levels. The main shortcoming of gene expression data is that it includes thousands of genes and a small number of samples. Abundant methods and techniques have been proposed for tumor classification using microarray gene expression data. Feature or gene selection methods can be used to mine the genes that directly involve in the classification and to eliminate irrelevant genes. In this paper statistical measures like T-Statistics, Signal-to-Noise Ratio (SNR) and F-Statistics are used to rank the genes. The ranked genes are used for further classification. Particle Swarm Optimization (PSO) algorithm and Shuffled Frog Leaping (SFL) algorithm are used to find the significant genes from the top-m ranked genes. The Naïve Bayes Classifier (NBC) is used to classify the samples based on the significant genes. The proposed work is applied on Lung and Ovarian datasets. The experimental results show that the proposed method achieves 100% accuracy in all the three datasets and the results are compared with previous works.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Microarray" title="Microarray">Microarray</a>, <a href="https://publications.waset.org/search?q=T-Statistics" title=" T-Statistics"> T-Statistics</a>, <a href="https://publications.waset.org/search?q=Signal-to-Noise%20Ratio" title=" Signal-to-Noise Ratio"> Signal-to-Noise Ratio</a>, <a href="https://publications.waset.org/search?q=FStatistics" title=" FStatistics"> FStatistics</a>, <a href="https://publications.waset.org/search?q=Particle%20Swarm%20Optimization" title=" Particle Swarm Optimization"> Particle Swarm Optimization</a>, <a href="https://publications.waset.org/search?q=Shuffled%20Frog%20Leaping" title=" Shuffled Frog Leaping"> Shuffled Frog Leaping</a>, <a href="https://publications.waset.org/search?q=Na%C3%AFve%20Bayes%20Classifier." title=" Na茂ve Bayes Classifier."> Na茂ve Bayes Classifier.</a> </p> <a href="https://publications.waset.org/9999853/statistical-measures-and-optimization-algorithms-for-gene-selection-in-lung-and-ovarian-tumor" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/9999853/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/9999853/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/9999853/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/9999853/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/9999853/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/9999853/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/9999853/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/9999853/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/9999853/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/9999853/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/9999853.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1945</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1102</span> A Simple Affymetrix Ratio-transformation Method Yields Comparable Expression Level Quantifications with cDNA Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Chintanu%20K.%20Sarmah">Chintanu K. Sarmah</a>, <a href="https://publications.waset.org/search?q=Sandhya%20Samarasinghe"> Sandhya Samarasinghe</a>, <a href="https://publications.waset.org/search?q=Don%20Kulasiri"> Don Kulasiri</a>, <a href="https://publications.waset.org/search?q=Daniel%20Catchpoole"> Daniel Catchpoole</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Gene expression profiling is rapidly evolving into a powerful technique for investigating tumor malignancies. The researchers are overwhelmed with the microarray-based platforms and methods that confer them the freedom to conduct large-scale gene expression profiling measurements. Simultaneously, investigations into cross-platform integration methods have started gaining momentum due to their underlying potential to help comprehend a myriad of broad biological issues in tumor diagnosis, prognosis, and therapy. However, comparing results from different platforms remains to be a challenging task as various inherent technical differences exist between the microarray platforms. In this paper, we explain a simple ratio-transformation method, which can provide some common ground for cDNA and Affymetrix platform towards cross-platform integration. The method is based on the characteristic data attributes of Affymetrix- and cDNA- platform. In the work, we considered seven childhood leukemia patients and their gene expression levels in either platform. With a dataset of 822 differentially expressed genes from both these platforms, we carried out a specific ratio-treatment to Affymetrix data, which subsequently showed an improvement in the relationship with the cDNA data. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Gene%20expression%20profiling" title="Gene expression profiling">Gene expression profiling</a>, <a href="https://publications.waset.org/search?q=microarray" title=" microarray"> microarray</a>, <a href="https://publications.waset.org/search?q=cDNA" title=" cDNA"> cDNA</a>, <a href="https://publications.waset.org/search?q=Affymetrix" title="Affymetrix">Affymetrix</a>, <a href="https://publications.waset.org/search?q=childhood%20leukaemia." title=" childhood leukaemia."> childhood leukaemia.</a> </p> <a href="https://publications.waset.org/7482/a-simple-affymetrix-ratio-transformation-method-yields-comparable-expression-level-quantifications-with-cdna-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/7482/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/7482/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/7482/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/7482/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/7482/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/7482/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/7482/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/7482/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/7482/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/7482/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/7482.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1522</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1101</span> Analysis of DNA Microarray Data using Association Rules: A Selective Study</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=M.%20Anandhavalli%20Gauthaman">M. Anandhavalli Gauthaman</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>DNA microarrays allow the measurement of expression levels for a large number of genes, perhaps all genes of an organism, within a number of different experimental samples. It is very much important to extract biologically meaningful information from this huge amount of expression data to know the current state of the cell because most cellular processes are regulated by changes in gene expression. Association rule mining techniques are helpful to find association relationship between genes. Numerous association rule mining algorithms have been developed to analyze and associate this huge amount of gene expression data. This paper focuses on some of the popular association rule mining algorithms developed to analyze gene expression data.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=DNA%20microarray" title="DNA microarray">DNA microarray</a>, <a href="https://publications.waset.org/search?q=gene%20expression" title=" gene expression"> gene expression</a>, <a href="https://publications.waset.org/search?q=association%20rule%20mining." title=" association rule mining."> association rule mining.</a> </p> <a href="https://publications.waset.org/5818/analysis-of-dna-microarray-data-using-association-rules-a-selective-study" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/5818/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/5818/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/5818/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/5818/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/5818/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/5818/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/5818/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/5818/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/5818/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/5818/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/5818.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">2145</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1100</span> Improved Wavelet Neural Networks for Early Cancer Diagnosis Using Clustering Algorithms</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Zarita%20Zainuddin">Zarita Zainuddin</a>, <a href="https://publications.waset.org/search?q=Ong%20Pauline"> Ong Pauline</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>Wavelet neural networks (WNNs) have emerged as a vital alternative to the vastly studied multilayer perceptrons (MLPs) since its first implementation. In this paper, we applied various clustering algorithms, namely, K-means (KM), Fuzzy C-means (FCM), symmetry-based K-means (SBKM), symmetry-based Fuzzy C-means (SBFCM) and modified point symmetry-based K-means (MPKM) clustering algorithms in choosing the translation parameter of a WNN. These modified WNNs are further applied to the heterogeneous cancer classification using benchmark microarray data and were compared against the conventional WNN with random initialization method. Experimental results showed that a WNN classifier with the MPKM algorithm is more precise than the conventional WNN as well as the WNNs with other clustering algorithms.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Clustering" title="Clustering">Clustering</a>, <a href="https://publications.waset.org/search?q=microarray" title=" microarray"> microarray</a>, <a href="https://publications.waset.org/search?q=symmetry" title=" symmetry"> symmetry</a>, <a href="https://publications.waset.org/search?q=wavelet%20neural%20networks." title=" wavelet neural networks."> wavelet neural networks.</a> </p> <a href="https://publications.waset.org/9166/improved-wavelet-neural-networks-for-early-cancer-diagnosis-using-clustering-algorithms" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/9166/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/9166/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/9166/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/9166/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/9166/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/9166/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/9166/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/9166/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/9166/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/9166/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/9166.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1616</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1099</span> Biomolecules Based Microarray for Screening Human Endothelial Cells Behavior</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Adel%20Dalilottojari">Adel Dalilottojari</a>, <a href="https://publications.waset.org/search?q=Bahman%20Delalat"> Bahman Delalat</a>, <a href="https://publications.waset.org/search?q=Frances%20J.%20Harding"> Frances J. Harding</a>, <a href="https://publications.waset.org/search?q=Michaelia%20P.%20Cockshell"> Michaelia P. Cockshell</a>, <a href="https://publications.waset.org/search?q=Claudine%20S.%20Bonder"> Claudine S. Bonder</a>, <a href="https://publications.waset.org/search?q=Nicolas%20H.%20Voelcker"> Nicolas H. Voelcker</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>Endothelial Progenitor Cell (EPC) based therapies continue to be of interest to treat ischemic events based on their proven role to promote blood vessel formation and thus tissue re-vascularisation. Current strategies for the production of clinical-grade EPCs requires the <em>in vitro</em> isolation of EPCs from peripheral blood followed by cell expansion to provide sufficient quantities EPCs for cell therapy. This study aims to examine the use of different biomolecules to significantly improve the current strategy of EPC capture and expansion on collagen type I (Col I). In this study, four different biomolecules were immobilised on a surface and then investigated for their capacity to support EPC capture and proliferation. First, a cell microarray platform was fabricated by coating a glass surface with epoxy functional allyl glycidyl ether plasma polymer (AGEpp) to mediate biomolecule binding. The four candidate biomolecules tested were Col I, collagen type II (Col II), collagen type IV (Col IV) and vascular endothelial growth factor A (VEGF-A), which were arrayed on the epoxy-functionalised surface using a non-contact printer. The surrounding area between the printed biomolecules was passivated with polyethylene glycol-bisamine (A-PEG) to prevent non-specific cell attachment. EPCs were seeded onto the microarray platform and cell numbers quantified after 1 h (to determine capture) and 72 h (to determine proliferation). All of the extracellular matrix (ECM) biomolecules printed demonstrated an ability to capture EPCs within 1 h of cell seeding with Col II exhibiting the highest level of attachment when compared to the other biomolecules. Interestingly, Col IV exhibited the highest increase in EPC expansion after 72 h when compared to Col I, Col II and VEGF-A. These results provide information for significant improvement in the capture and expansion of human EPC for further application.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Cardiovascular%20disease" title="Cardiovascular disease">Cardiovascular disease</a>, <a href="https://publications.waset.org/search?q=cell%20microarray%20platform" title=" cell microarray platform"> cell microarray platform</a>, <a href="https://publications.waset.org/search?q=cell%20therapy" title=" cell therapy"> cell therapy</a>, <a href="https://publications.waset.org/search?q=endothelial%20progenitor%20cells" title=" endothelial progenitor cells"> endothelial progenitor cells</a>, <a href="https://publications.waset.org/search?q=high%20throughput%20screening." title=" high throughput screening."> high throughput screening.</a> </p> <a href="https://publications.waset.org/10006121/biomolecules-based-microarray-for-screening-human-endothelial-cells-behavior" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10006121/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10006121/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10006121/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10006121/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10006121/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10006121/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/10006121/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/10006121/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/10006121/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/10006121/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/10006121.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1420</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1098</span> Apoptosis Pathway Targeted by Thymoquinone in MCF7 Breast Cancer Cell Line</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=M.%20Marjaneh">M. Marjaneh</a>, <a href="https://publications.waset.org/search?q=M.%20Y.%20Narazah"> M. Y. Narazah</a>, <a href="https://publications.waset.org/search?q=H.%20Shahrul"> H. Shahrul</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>Array-based gene expression analysis is a powerful tool to profile expression of genes and to generate information on therapeutic effects of new anti-cancer compounds. Anti-apoptotic effect of thymoquinone was studied in MCF7 breast cancer cell line using gene expression profiling with cDNA microarray. The purity and yield of RNA samples were determined using RNeasyPlus Mini kit. The Agilent RNA 6000 NanoLabChip kit evaluated the quantity of the RNA samples. AffinityScript RT oligo-dT promoter primer was used to generate cDNA strands. T7 RNA polymerase was used to convert cDNA to cRNA. The cRNA samples and human universal reference RNA were labelled with Cy-3-CTP and Cy-5-CTP, respectively. Feature Extraction and GeneSpring softwares analysed the data. The single experiment analysis revealed involvement of 64 pathways with up-regulated genes and 78 pathways with downregulated genes. The MAPK and p38-MAPK pathways were inhibited due to the up-regulation of PTPRR gene. The inhibition of p38-MAPK suggested up-regulation of TGF-ß pathway. Inhibition of p38-MAPK caused up-regulation of TP53 and down-regulation of Bcl2 genes indicating involvement of intrinsic apoptotic pathway. Down-regulation of CARD16 gene as an adaptor molecule regulated CASP1 and suggested necrosis-like programmed cell death and involvement of caspase in apoptosis. Furthermore, down-regulation of GPCR, EGF-EGFR signalling pathways suggested reduction of ER. Involvement of AhR pathway which control cytochrome P450 and glucuronidation pathways showed metabolism of Thymoquinone. The findings showed differential expression of several genes in apoptosis pathways with thymoquinone treatment in estrogen receptor-positive breast cancer cells.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=CARD16" title="CARD16">CARD16</a>, <a href="https://publications.waset.org/search?q=CASP10" title=" CASP10"> CASP10</a>, <a href="https://publications.waset.org/search?q=cDNA%20microarray" title=" cDNA microarray"> cDNA microarray</a>, <a href="https://publications.waset.org/search?q=PTPRR" title=" PTPRR"> PTPRR</a>, <a href="https://publications.waset.org/search?q=Thymoquinone." title=" Thymoquinone."> Thymoquinone.</a> </p> <a href="https://publications.waset.org/10001014/apoptosis-pathway-targeted-by-thymoquinone-in-mcf7-breast-cancer-cell-line" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10001014/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10001014/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10001014/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10001014/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10001014/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10001014/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/10001014/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/10001014/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/10001014/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/10001014/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/10001014.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">2289</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1097</span> First Studies of the Influence of Single Gene Perturbations on the Inference of Genetic Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Frank%20Emmert-Streib">Frank Emmert-Streib</a>, <a href="https://publications.waset.org/search?q=Matthias%20Dehmer"> Matthias Dehmer</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Inferring the network structure from time series data is a hard problem, especially if the time series is short and noisy. DNA microarray is a technology allowing to monitor the mRNA concentration of thousands of genes simultaneously that produces data of these characteristics. In this study we try to investigate the influence of the experimental design on the quality of the result. More precisely, we investigate the influence of two different types of random single gene perturbations on the inference of genetic networks from time series data. To obtain an objective quality measure for this influence we simulate gene expression values with a biologically plausible model of a known network structure. Within this framework we study the influence of single gene knock-outs in opposite to linearly controlled expression for single genes on the quality of the infered network structure. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Dynamic%20Bayesian%20networks" title="Dynamic Bayesian networks">Dynamic Bayesian networks</a>, <a href="https://publications.waset.org/search?q=microarray%20data" title=" microarray data"> microarray data</a>, <a href="https://publications.waset.org/search?q=structure%20learning" title=" structure learning"> structure learning</a>, <a href="https://publications.waset.org/search?q=Markov%20chain%20Monte%20Carlo." title=" Markov chain Monte Carlo."> Markov chain Monte Carlo.</a> </p> <a href="https://publications.waset.org/15602/first-studies-of-the-influence-of-single-gene-perturbations-on-the-inference-of-genetic-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/15602/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/15602/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/15602/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/15602/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/15602/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/15602/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/15602/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/15602/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/15602/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/15602/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/15602.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1550</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1096</span> A Phenomic Algorithm for Reconstruction of Gene Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Rio%20G.%20L.%20D%27Souza">Rio G. L. D'Souza</a>, <a href="https://publications.waset.org/search?q=K.%20Chandra%20Sekaran"> K. Chandra Sekaran</a>, <a href="https://publications.waset.org/search?q=A.%20Kandasamy"> A. Kandasamy</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>The goal of Gene Expression Analysis is to understand the processes that underlie the regulatory networks and pathways controlling inter-cellular and intra-cellular activities. In recent times microarray datasets are extensively used for this purpose. The scope of such analysis has broadened in recent times towards reconstruction of gene networks and other holistic approaches of Systems Biology. Evolutionary methods are proving to be successful in such problems and a number of such methods have been proposed. However all these methods are based on processing of genotypic information. Towards this end, there is a need to develop evolutionary methods that address phenotypic interactions together with genotypic interactions. We present a novel evolutionary approach, called Phenomic algorithm, wherein the focus is on phenotypic interaction. We use the expression profiles of genes to model the interactions between them at the phenotypic level. We apply this algorithm to the yeast sporulation dataset and show that the algorithm can identify gene networks with relative ease.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Evolutionary%20computing" title="Evolutionary computing">Evolutionary computing</a>, <a href="https://publications.waset.org/search?q=gene%20expression%20analysis" title=" gene expression analysis"> gene expression analysis</a>, <a href="https://publications.waset.org/search?q=gene%20networks" title=" gene networks"> gene networks</a>, <a href="https://publications.waset.org/search?q=microarray%20data%20analysis" title=" microarray data analysis"> microarray data analysis</a>, <a href="https://publications.waset.org/search?q=phenomic%20algorithms." title=" phenomic algorithms."> phenomic algorithms.</a> </p> <a href="https://publications.waset.org/10185/a-phenomic-algorithm-for-reconstruction-of-gene-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10185/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10185/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10185/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10185/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10185/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10185/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/10185/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/10185/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a 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