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network</title> <meta name="description" content="Search results for: gene network"> <meta name="keywords" content="gene network"> <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 d-lg-none navbar-toggler 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style="font-size:.9rem"><span class="badge badge-info">6167</span> Construction of the Large Scale Biological Networks from Microarrays</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Fadhl%20Alakwaa">Fadhl Alakwaa</a> </p> <p class="card-text"><strong>Abstract:</strong></p> One of the sustainable goals of the system biology is understanding gene-gene interactions. Hence, gene regulatory networks (GRN) need to be constructed for understanding the disease ontology and to reduce the cost of drug development. To construct gene regulatory from gene expression we need to overcome many challenges such as data denoising and dimensionality. In this paper, we develop an integrated system to reduce data dimension and remove the noise. The generated network from our system was validated via available interaction databases and was compared to previous methods. The result revealed the performance of our proposed method. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=gene%20regulatory%20network" title="gene regulatory network">gene regulatory network</a>, <a href="https://publications.waset.org/abstracts/search?q=biclustering" title=" biclustering"> biclustering</a>, <a href="https://publications.waset.org/abstracts/search?q=denoising" title=" denoising"> denoising</a>, <a href="https://publications.waset.org/abstracts/search?q=system%20biology" title=" system biology"> system biology</a> </p> <a href="https://publications.waset.org/abstracts/74607/construction-of-the-large-scale-biological-networks-from-microarrays" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/74607.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">239</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6166</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/abstracts/search?q=Mona%20Heydari">Mona Heydari</a>, <a href="https://publications.waset.org/abstracts/search?q=Ehsan%20Motamedian"> Ehsan Motamedian</a>, <a href="https://publications.waset.org/abstracts/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/abstracts/search?q=metabolic%20network" title="metabolic network">metabolic network</a>, <a href="https://publications.waset.org/abstracts/search?q=gene%20knockout" title=" gene knockout"> gene knockout</a>, <a href="https://publications.waset.org/abstracts/search?q=flux%20balance%20analysis" title=" flux balance analysis"> flux balance analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=microarray%20data" title=" microarray data"> microarray data</a>, <a href="https://publications.waset.org/abstracts/search?q=integration" title=" integration"> integration</a> </p> <a href="https://publications.waset.org/abstracts/15750/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/abstracts/15750.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">579</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6165</span> Gene Names Identity Recognition Using Siamese Network for Biomedical Publications</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Micheal%20Olaolu%20Arowolo">Micheal Olaolu Arowolo</a>, <a href="https://publications.waset.org/abstracts/search?q=Muhammad%20Azam"> Muhammad Azam</a>, <a href="https://publications.waset.org/abstracts/search?q=Fei%20He"> Fei He</a>, <a href="https://publications.waset.org/abstracts/search?q=Mihail%20Popescu"> Mihail Popescu</a>, <a href="https://publications.waset.org/abstracts/search?q=Dong%20Xu"> Dong Xu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> As the quantity of biological articles rises, so does the number of biological route figures. Each route figure shows gene names and relationships. Annotating pathway diagrams manually is time-consuming. Advanced image understanding models could speed up curation, but they must be more precise. There is rich information in biological pathway figures. The first step to performing image understanding of these figures is to recognize gene names automatically. Classical optical character recognition methods have been employed for gene name recognition, but they are not optimized for literature mining data. This study devised a method to recognize an image bounding box of gene name as a photo using deep Siamese neural network models to outperform the existing methods using ResNet, DenseNet and Inception architectures, the results obtained about 84% accuracy. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=biological%20pathway" title="biological pathway">biological pathway</a>, <a href="https://publications.waset.org/abstracts/search?q=gene%20identification" title=" gene identification"> gene identification</a>, <a href="https://publications.waset.org/abstracts/search?q=object%20detection" title=" object detection"> object detection</a>, <a href="https://publications.waset.org/abstracts/search?q=Siamese%20network" title=" Siamese network"> Siamese network</a> </p> <a href="https://publications.waset.org/abstracts/160725/gene-names-identity-recognition-using-siamese-network-for-biomedical-publications" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/160725.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">292</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6164</span> Recognition of Gene Names from Gene Pathway Figures Using Siamese Network</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Muhammad%20Azam">Muhammad Azam</a>, <a href="https://publications.waset.org/abstracts/search?q=Micheal%20Olaolu%20Arowolo"> Micheal Olaolu Arowolo</a>, <a href="https://publications.waset.org/abstracts/search?q=Fei%20He"> Fei He</a>, <a href="https://publications.waset.org/abstracts/search?q=Mihail%20Popescu"> Mihail Popescu</a>, <a href="https://publications.waset.org/abstracts/search?q=Dong%20Xu"> Dong Xu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The number of biological papers is growing quickly, which means that the number of biological pathway figures in those papers is also increasing quickly. Each pathway figure shows extensive biological information, like the names of genes and how the genes are related. However, manually annotating pathway figures takes a lot of time and work. Even though using advanced image understanding models could speed up the process of curation, these models still need to be made more accurate. To improve gene name recognition from pathway figures, we applied a Siamese network to map image segments to a library of pictures containing known genes in a similar way to person recognition from photos in many photo applications. We used a triple loss function and a triplet spatial pyramid pooling network by combining the triplet convolution neural network and the spatial pyramid pooling (TSPP-Net). We compared VGG19 and VGG16 as the Siamese network model. VGG16 achieved better performance with an accuracy of 93%, which is much higher than OCR results. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=biological%20pathway" title="biological pathway">biological pathway</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20understanding" title=" image understanding"> image understanding</a>, <a href="https://publications.waset.org/abstracts/search?q=gene%20name%20recognition" title=" gene name recognition"> gene name recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=object%20detection" title=" object detection"> object detection</a>, <a href="https://publications.waset.org/abstracts/search?q=Siamese%20network" title=" Siamese network"> Siamese network</a>, <a href="https://publications.waset.org/abstracts/search?q=VGG" title=" VGG"> VGG</a> </p> <a href="https://publications.waset.org/abstracts/160723/recognition-of-gene-names-from-gene-pathway-figures-using-siamese-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/160723.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">291</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6163</span> Improved Predictive Models for the IRMA Network Using Nonlinear Optimisation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Vishwesh%20Kulkarni">Vishwesh Kulkarni</a>, <a href="https://publications.waset.org/abstracts/search?q=Nikhil%20Bellarykar"> Nikhil Bellarykar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Cellular complexity stems from the interactions among thousands of different molecular species. Thanks to the emerging fields of systems and synthetic biology, scientists are beginning to unravel these regulatory, signaling, and metabolic interactions and to understand their coordinated action. Reverse engineering of biological networks has has several benefits but a poor quality of data combined with the difficulty in reproducing it limits the applicability of these methods. A few years back, many of the commonly used predictive algorithms were tested on a network constructed in the yeast Saccharomyces cerevisiae (S. cerevisiae) to resolve this issue. The network was a synthetic network of five genes regulating each other for the so-called in vivo reverse-engineering and modeling assessment (IRMA). The network was constructed in S. cereviase since it is a simple and well characterized organism. The synthetic network included a variety of regulatory interactions, thus capturing the behaviour of larger eukaryotic gene networks on a smaller scale. We derive a new set of algorithms by solving a nonlinear optimization problem and show how these algorithms outperform other algorithms on these datasets. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=synthetic%20gene%20network" title="synthetic gene network">synthetic gene network</a>, <a href="https://publications.waset.org/abstracts/search?q=network%20identification" title=" network identification"> network identification</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=nonlinear%20modeling" title=" nonlinear modeling"> nonlinear modeling</a> </p> <a href="https://publications.waset.org/abstracts/94037/improved-predictive-models-for-the-irma-network-using-nonlinear-optimisation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/94037.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">156</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6162</span> Effects of Epinephrine on Gene Expressions during the Metamorphosis of Pacific Oyster Crassostrea gigas</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Fei%20Xu">Fei Xu</a>, <a href="https://publications.waset.org/abstracts/search?q=Guofan%20Zhang"> Guofan Zhang</a>, <a href="https://publications.waset.org/abstracts/search?q=Xiao%20Liu"> Xiao Liu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Many major marine invertebrate phyla are characterized by indirect development. These animals transit from planktonic larvae to benthic adults via settlement and metamorphosis, which has many advantages for organisms to adapt marine environment. Studying the biological process of metamorphosis is thus a key to understand the origin and evolution of indirect development. Although the mechanism of metamorphosis has been largely studied on their relationships with the marine environment, microorganisms, as well as the neurohormones, little is known on the gene regulation network (GRN) during metamorphosis. We treated competent oyster pediveligers with epinephrine, which was known to be able to effectively induce oyster metamorphosis, and analyzed the dynamics of gene and proteins with transcriptomics and proteomics methods. The result indicated significant upregulation of protein synthesis system, as well as some transcription factors including Homeobox, basic helix-loop-helix, and nuclear receptors. The result suggested the GRN complexity of the transition stage during oyster metamorphosis. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=indirect%20development" title="indirect development">indirect development</a>, <a href="https://publications.waset.org/abstracts/search?q=gene%20regulation%20network" title=" gene regulation network"> gene regulation network</a>, <a href="https://publications.waset.org/abstracts/search?q=protein%20synthesis" title=" protein synthesis"> protein synthesis</a>, <a href="https://publications.waset.org/abstracts/search?q=transcription%20factors" title=" transcription factors"> transcription factors</a> </p> <a href="https://publications.waset.org/abstracts/104901/effects-of-epinephrine-on-gene-expressions-during-the-metamorphosis-of-pacific-oyster-crassostrea-gigas" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/104901.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">140</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6161</span> Elucidation of the Sequential Transcriptional Activity in Escherichia coli Using Time-Series RNA-Seq Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Pui%20Shan%20Wong">Pui Shan Wong</a>, <a href="https://publications.waset.org/abstracts/search?q=Kosuke%20Tashiro"> Kosuke Tashiro</a>, <a href="https://publications.waset.org/abstracts/search?q=Satoru%20Kuhara"> Satoru Kuhara</a>, <a href="https://publications.waset.org/abstracts/search?q=Sachiyo%20Aburatani"> Sachiyo Aburatani</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Functional genomics and gene regulation inference has readily expanded our knowledge and understanding of gene interactions with regards to expression regulation. With the advancement of transcriptome sequencing in time-series comes the ability to study the sequential changes of the transcriptome. This method presented here works to augment existing regulation networks accumulated in literature with transcriptome data gathered from time-series experiments to construct a sequential representation of transcription factor activity. This method is applied on a time-series RNA-Seq data set from Escherichia coli as it transitions from growth to stationary phase over five hours. Investigations are conducted on the various metabolic activities in gene regulation processes by taking advantage of the correlation between regulatory gene pairs to examine their activity on a dynamic network. Especially, the changes in metabolic activity during phase transition are analyzed with focus on the pagP gene as well as other associated transcription factors. The visualization of the sequential transcriptional activity is used to describe the change in metabolic pathway activity originating from the pagP transcription factor, phoP. The results show a shift from amino acid and nucleic acid metabolism, to energy metabolism during the transition to stationary phase in E. coli. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Escherichia%20coli" title="Escherichia coli">Escherichia coli</a>, <a href="https://publications.waset.org/abstracts/search?q=gene%20regulation" title=" gene regulation"> gene regulation</a>, <a href="https://publications.waset.org/abstracts/search?q=network" title=" network"> network</a>, <a href="https://publications.waset.org/abstracts/search?q=time-series" title=" time-series"> time-series</a> </p> <a href="https://publications.waset.org/abstracts/65272/elucidation-of-the-sequential-transcriptional-activity-in-escherichia-coli-using-time-series-rna-seq-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/65272.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">372</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6160</span> Intelligent CRISPR Design for Bone Regeneration</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yu-Chen%20Hu">Yu-Chen Hu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Gene editing by CRISPR and gene regulation by microRNA or CRISPR activation have dramatically changed the way to manipulate cellular gene expression and cell fate. In recent years, various gene editing and gene manipulation technologies have been applied to control stem cell differentiation to enhance tissue regeneration. This research will focus on how to develop CRISPR, CRISPR activation (CRISPRa), CRISPR inhibition (CRISPRi), as well as bi-directional CRISPR-AI gene regulation technologies to control cell differentiation and bone regeneration. Moreover, in this study, CRISPR/Cas13d-mediated RNA editng for miRNA editing and bone regeneration will be discussed. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=gene%20therapy" title="gene therapy">gene therapy</a>, <a href="https://publications.waset.org/abstracts/search?q=bone%20regeneration" title=" bone regeneration"> bone regeneration</a>, <a href="https://publications.waset.org/abstracts/search?q=stem%20cell" title=" stem cell"> stem cell</a>, <a href="https://publications.waset.org/abstracts/search?q=CRISPR" title=" CRISPR"> CRISPR</a>, <a href="https://publications.waset.org/abstracts/search?q=gene%20regulation" title=" gene regulation"> gene regulation</a> </p> <a href="https://publications.waset.org/abstracts/168750/intelligent-crispr-design-for-bone-regeneration" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/168750.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">90</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6159</span> SCANet: A Workflow for Single-Cell Co-Expression Based Analysis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mhaned%20Oubounyt">Mhaned Oubounyt</a>, <a href="https://publications.waset.org/abstracts/search?q=Jan%20Baumbach"> Jan Baumbach</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Differences in co-expression networks between two or multiple cells (sub)types across conditions is a pressing problem in single-cell RNA sequencing (scRNA-seq). A key challenge is to define those co-variations that differ between or among cell types and/or conditions and phenotypes to examine small regulatory networks that can explain mechanistic differences. To this end, we developed SCANet, an all-in-one Python package that uses state-of-the-art algorithms to facilitate the workflow of a combined single-cell GCN (Gene Correlation Network) and GRN (Gene Regulatory Networks) pipeline, including inference of gene co-expression modules from scRNA-seq, followed by trait and cell type associations, hub gene detection, co-regulatory networks, and drug-gene interactions. In an example case, we illustrate how SCANet can be applied to identify regulatory drivers behind a cytokine storm associated with mortality in patients with acute respiratory illness. SCANet is available as a free, open-source, and user-friendly Python package that can be easily integrated into systems biology pipelines. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=single-cell" title="single-cell">single-cell</a>, <a href="https://publications.waset.org/abstracts/search?q=co-expression%20networks" title=" co-expression networks"> co-expression networks</a>, <a href="https://publications.waset.org/abstracts/search?q=drug-gene%20interactions" title=" drug-gene interactions"> drug-gene interactions</a>, <a href="https://publications.waset.org/abstracts/search?q=co-regulatory%20networks" title=" co-regulatory networks"> co-regulatory networks</a> </p> <a href="https://publications.waset.org/abstracts/161853/scanet-a-workflow-for-single-cell-co-expression-based-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/161853.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">150</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6158</span> A Local Tensor Clustering Algorithm to Annotate Uncharacterized Genes with Many Biological Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Paul%20Shize%20Li">Paul Shize Li</a>, <a href="https://publications.waset.org/abstracts/search?q=Frank%20Alber"> Frank Alber</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A fundamental task of clinical genomics is to unravel the functions of genes and their associations with disorders. Although experimental biology has made efforts to discover and elucidate the molecular mechanisms of individual genes in the past decades, still about 40% of human genes have unknown functions, not to mention the diseases they may be related to. For those biologists who are interested in a particular gene with unknown functions, a powerful computational method tailored for inferring the functions and disease relevance of uncharacterized genes is strongly needed. Studies have shown that genes strongly linked to each other in multiple biological networks are more likely to have similar functions. This indicates that the densely connected subgraphs in multiple biological networks are useful in the functional and phenotypic annotation of uncharacterized genes. Therefore, in this work, we have developed an integrative network approach to identify the frequent local clusters, which are defined as those densely connected subgraphs that frequently occur in multiple biological networks and consist of the query gene that has few or no disease or function annotations. This is a local clustering algorithm that models multiple biological networks sharing the same gene set as a three-dimensional matrix, the so-called tensor, and employs the tensor-based optimization method to efficiently find the frequent local clusters. Specifically, massive public gene expression data sets that comprehensively cover dynamic, physiological, and environmental conditions are used to generate hundreds of gene co-expression networks. By integrating these gene co-expression networks, for a given uncharacterized gene that is of biologist’s interest, the proposed method can be applied to identify the frequent local clusters that consist of this uncharacterized gene. Finally, those frequent local clusters are used for function and disease annotation of this uncharacterized gene. This local tensor clustering algorithm outperformed the competing tensor-based algorithm in both module discovery and running time. We also demonstrated the use of the proposed method on real data of hundreds of gene co-expression data and showed that it can comprehensively characterize the query gene. Therefore, this study provides a new tool for annotating the uncharacterized genes and has great potential to assist clinical genomic diagnostics. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=local%20tensor%20clustering" title="local tensor clustering">local tensor clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=query%20gene" title=" query gene"> query gene</a>, <a href="https://publications.waset.org/abstracts/search?q=gene%20co-expression%20network" title=" gene co-expression network"> gene co-expression network</a>, <a href="https://publications.waset.org/abstracts/search?q=gene%20annotation" title=" gene annotation"> gene annotation</a> </p> <a href="https://publications.waset.org/abstracts/155115/a-local-tensor-clustering-algorithm-to-annotate-uncharacterized-genes-with-many-biological-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/155115.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">168</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6157</span> Identification of Mx Gene Polymorphism in Indragiri Hulu duck by PCR-RFLP</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Restu%20Misrianti">Restu Misrianti</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The amino acid variation of Asn (allele A) at position 631 in Mx gene was specific to positive antiviral to avian viral desease. This research was aimed at identifying polymorphism of Mx gene in duck using molecular technique. Polymerase Chain Reaction-Restriction Fragment Length Polymorphism (PCR-RFLP) technique was used to select the genotype of AA, AG and GG. There were thirteen duck from Indragiri Hulu regency (Riau Province) used in this experiment. DNA amplification results showed that the Mx gene in duck is found in a 73 bp fragment. Mx gene in duck did not show any polymorphism. The frequency of the resistant allele (AA) was 0%, while the frequency of the susceptible allele (GG) was 100%. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=duck" title="duck">duck</a>, <a href="https://publications.waset.org/abstracts/search?q=Mx%20gene" title=" Mx gene"> Mx gene</a>, <a href="https://publications.waset.org/abstracts/search?q=PCR" title=" PCR"> PCR</a>, <a href="https://publications.waset.org/abstracts/search?q=RFLP" title=" RFLP"> RFLP</a> </p> <a href="https://publications.waset.org/abstracts/37764/identification-of-mx-gene-polymorphism-in-indragiri-hulu-duck-by-pcr-rflp" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/37764.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">324</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6156</span> Computational Model for Predicting Effective siRNA Sequences Using Whole Stacking Energy (ΔG) for Gene Silencing</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Reena%20Murali">Reena Murali</a>, <a href="https://publications.waset.org/abstracts/search?q=David%20Peter%20S."> David Peter S.</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The small interfering RNA (siRNA) alters the regulatory role of mRNA during gene expression by translational inhibition. Recent studies shows that up regulation of mRNA cause serious diseases like Cancer. So designing effective siRNA with good knockdown effects play an important role in gene silencing. Various siRNA design tools had been developed earlier. In this work, we are trying to analyze the existing good scoring second generation siRNA predicting tools and to optimize the efficiency of siRNA prediction by designing a computational model using Artificial Neural Network and whole stacking energy (ΔG), which may help in gene silencing and drug design in cancer therapy. Our model is trained and tested against a large data set of siRNA sequences. Validation of our results is done by finding correlation coefficient of experimental versus observed inhibition efficacy of siRNA. We achieved a correlation coefficient of 0.727 in our previous computational model and we could improve the correlation coefficient up to 0.753 when the threshold of whole tacking energy is greater than or equal to -32.5 kcal/mol. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=artificial%20neural%20network" title="artificial neural network">artificial neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=double%20stranded%20RNA" title=" double stranded RNA"> double stranded RNA</a>, <a href="https://publications.waset.org/abstracts/search?q=RNA%20interference" title=" RNA interference"> RNA interference</a>, <a href="https://publications.waset.org/abstracts/search?q=short%20interfering%20RNA" title=" short interfering RNA"> short interfering RNA</a> </p> <a href="https://publications.waset.org/abstracts/16841/computational-model-for-predicting-effective-sirna-sequences-using-whole-stacking-energy-dg-for-gene-silencing" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/16841.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">526</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6155</span> Ordinary Differentiation Equations (ODE) Reconstruction of High-Dimensional Genetic Networks through Game Theory with Application to Dissecting Tree Salt Tolerance</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Libo%20Jiang">Libo Jiang</a>, <a href="https://publications.waset.org/abstracts/search?q=Huan%20Li"> Huan Li</a>, <a href="https://publications.waset.org/abstracts/search?q=Rongling%20Wu"> Rongling Wu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Ordinary differentiation equations (ODE) have proven to be powerful for reconstructing precise and informative gene regulatory networks (GRNs) from dynamic gene expression data. However, joint modeling and analysis of all genes, essential for the systematical characterization of genetic interactions, are challenging due to high dimensionality and a complex pattern of genetic regulation including activation, repression, and antitermination. Here, we address these challenges by unifying variable selection and game theory through ODE. Each gene within a GRN is co-expressed with its partner genes in a way like a game of multiple players, each of which tends to choose an optimal strategy to maximize its “fitness” across the whole network. Based on this unifying theory, we designed and conducted a real experiment to infer salt tolerance-related GRNs for Euphrates poplar, a hero tree that can grow in the saline desert. The pattern and magnitude of interactions between several hub genes within these GRNs were found to determine the capacity of Euphrates poplar to resist to saline stress. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=gene%20regulatory%20network" title="gene regulatory network">gene regulatory network</a>, <a href="https://publications.waset.org/abstracts/search?q=ordinary%20differential%20equation" title=" ordinary differential equation"> ordinary differential equation</a>, <a href="https://publications.waset.org/abstracts/search?q=game%20theory" title=" game theory"> game theory</a>, <a href="https://publications.waset.org/abstracts/search?q=LASSO" title=" LASSO"> LASSO</a>, <a href="https://publications.waset.org/abstracts/search?q=saline%20resistance" title=" saline resistance"> saline resistance</a> </p> <a href="https://publications.waset.org/abstracts/65286/ordinary-differentiation-equations-ode-reconstruction-of-high-dimensional-genetic-networks-through-game-theory-with-application-to-dissecting-tree-salt-tolerance" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/65286.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">639</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6154</span> The Interplay between Autophagy and Macrophages' Polarization in Wound Healing: A Genetic Regulatory Network Analysis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mayada%20Mazher">Mayada Mazher</a>, <a href="https://publications.waset.org/abstracts/search?q=Ahmed%20Moustafa"> Ahmed Moustafa</a>, <a href="https://publications.waset.org/abstracts/search?q=Ahmed%20Abdellatif"> Ahmed Abdellatif</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Background: Autophagy is a eukaryotic, highly conserved catabolic process implicated in many pathophysiologies such as wound healing. Autophagy-associated genes serve as a scaffolding platform for signal transduction of macrophage polarization during the inflammatory phase of wound healing and tissue repair process. In the current study, we report a model for the interplay between autophagy-associated genes and macrophages polarization associated genes. Methods: In silico analysis was performed on 249 autophagy-related genes retrieved from the public autophagy database and gene expression data retrieved from Gene Expression Omnibus (GEO); GSE81922 and GSE69607 microarray data macrophages polarization 199 DEGS. An integrated protein-protein interaction network was constructed for autophagy and macrophage gene sets. The gene sets were then used for GO terms pathway enrichment analysis. Common transcription factors for autophagy and macrophages' polarization were identified. Finally, microRNAs enriched in both autophagy and macrophages were predicated. Results: In silico prediction of common transcription factors in DEGs macrophages and autophagy gene sets revealed a new role for the transcription factors, HOMEZ, GABPA, ELK1 and REL, that commonly regulate macrophages associated genes: IL6,IL1M, IL1B, NOS1, SOC3 and autophagy-related genes: Atg12, Rictor, Rb1cc1, Gaparab1, Atg16l1. Conclusions: Autophagy and macrophages' polarization are interdependent cellular processes, and both autophagy-related proteins and macrophages' polarization related proteins coordinate in tissue remodelling via transcription factors and microRNAs regulatory network. The current work highlights a potential new role for transcription factors HOMEZ, GABPA, ELK1 and REL in wound healing. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=autophagy%20related%20proteins" title="autophagy related proteins">autophagy related proteins</a>, <a href="https://publications.waset.org/abstracts/search?q=integrated%20network%20analysis" title=" integrated network analysis"> integrated network analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=macrophages%20polarization%20M1%20and%20M2" title=" macrophages polarization M1 and M2"> macrophages polarization M1 and M2</a>, <a href="https://publications.waset.org/abstracts/search?q=tissue%20remodelling" title=" tissue remodelling"> tissue remodelling</a> </p> <a href="https://publications.waset.org/abstracts/108802/the-interplay-between-autophagy-and-macrophages-polarization-in-wound-healing-a-genetic-regulatory-network-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/108802.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">152</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6153</span> Macronutrients and the FTO Gene Expression in Hypothalamus: A Systematic Review of Experimental Studies</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Saeid%20Doaei">Saeid Doaei</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The various studies have examined the relationship between FTO gene expression and macronutrients levels. In order to obtain better viewpoint from this interactions, all of the existing studies were reviewed systematically. All published papers have been obtained and reviewed using standard and sensitive keywords from databases such as CINAHL, Embase, PubMed, PsycInfo, and the Cochrane, from 1990 to 2016. The results indicated that all of 6 studies that met the inclusion criteria (from a total of 428 published article) found FTO gene expression changes at short-term follow-ups. Four of six studies found an increased FTO gene expression after calorie restriction, while two of them indicated decreased FTO gene expression. The effect of protein, carbohydrate and fat were separately assessed and suggested by all of six studies. In conclusion, the level of FTO gene expression in hypothalamus is related to macronutrients levels. Future research should evaluate the long-term impact of dietary interventions. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=obesity" title="obesity">obesity</a>, <a href="https://publications.waset.org/abstracts/search?q=gene%20expression" title=" gene expression"> gene expression</a>, <a href="https://publications.waset.org/abstracts/search?q=FTO" title=" FTO"> FTO</a>, <a href="https://publications.waset.org/abstracts/search?q=macronutrients" title=" macronutrients"> macronutrients</a> </p> <a href="https://publications.waset.org/abstracts/71018/macronutrients-and-the-fto-gene-expression-in-hypothalamus-a-systematic-review-of-experimental-studies" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/71018.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">267</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6152</span> Integrating Dynamic Brain Connectivity and Transcriptomic Imaging in Major Depressive Disorder</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Qingjin%20Liu">Qingjin Liu</a>, <a href="https://publications.waset.org/abstracts/search?q=Jinpeng%20Niu"> Jinpeng Niu</a>, <a href="https://publications.waset.org/abstracts/search?q=Kangjia%20Chen"> Kangjia Chen</a>, <a href="https://publications.waset.org/abstracts/search?q=Jiao%20Li"> Jiao Li</a>, <a href="https://publications.waset.org/abstracts/search?q=Huafu%20Chen"> Huafu Chen</a>, <a href="https://publications.waset.org/abstracts/search?q=Wei%20Liao"> Wei Liao</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Functional connectomics is essential in cognitive science and neuropsychiatry, offering insights into the brain's complex network structures and dynamic interactions. Although neuroimaging has uncovered functional connectivity issues in Major Depressive Disorder (MDD) patients, the dynamic shifts in connectome topology and their link to gene expression are yet to be fully understood. To explore the differences in dynamic connectome topology between MDD patients and healthy individuals, we conducted an extensive analysis of resting-state functional magnetic resonance imaging (fMRI) data from 434 participants (226 MDD patients and 208 controls). We used multilayer network models to evaluate brain module dynamics and examined the association between whole-brain gene expression and dynamic module variability in MDD using publicly available transcriptomic data. Our findings revealed that compared to healthy individuals, MDD patients showed lower global mean values and higher standard deviations, indicating unstable patterns and increased regional differentiation. Notably, MDD patients exhibited more frequent module switching, primarily within the executive control network (ECN), particularly in the left dorsolateral prefrontal cortex and right fronto-insular regions, whereas the default mode network (DMN), including the superior frontal gyrus, temporal lobe, and right medial prefrontal cortex, displayed lower variability. These brain dynamics predicted the severity of depressive symptoms. Analyzing human brain gene expression data, we found that the spatial distribution of MDD-related gene expression correlated with dynamic module differences. Cell type-specific gene analyses identified oligodendrocytes (OPCs) as major contributors to the transcriptional relationships underlying module variability in MDD. To the best of our knowledge, this is the first comprehensive description of altered brain module dynamics in MDD patients linked to depressive symptom severity and changes in whole-brain gene expression profiles. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=major%20depressive%20disorder" title="major depressive disorder">major depressive disorder</a>, <a href="https://publications.waset.org/abstracts/search?q=module%20dynamics" title=" module dynamics"> module dynamics</a>, <a href="https://publications.waset.org/abstracts/search?q=magnetic%20resonance%20imaging" title=" magnetic resonance imaging"> magnetic resonance imaging</a>, <a href="https://publications.waset.org/abstracts/search?q=transcriptomic" title=" transcriptomic"> transcriptomic</a> </p> <a href="https://publications.waset.org/abstracts/190173/integrating-dynamic-brain-connectivity-and-transcriptomic-imaging-in-major-depressive-disorder" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/190173.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">25</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6151</span> Identification of Significant Genes in Rheumatoid Arthritis, Melanoma Metastasis, Ulcerative Colitis and Crohn’s Disease</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Krishna%20Pal%20Singh">Krishna Pal Singh</a>, <a href="https://publications.waset.org/abstracts/search?q=Shailendra%20Kumar%20Gupta"> Shailendra Kumar Gupta</a>, <a href="https://publications.waset.org/abstracts/search?q=Olaf%20Wolkenhauer"> Olaf Wolkenhauer</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Background: Our study aimed to identify common genes and potential targets across the four diseases, which include rheumatoid arthritis, melanoma metastasis, ulcerative colitis, and Crohn’s disease. We used a network and systems biology approach to identify the hub gene, which can act as a potential target for all four disease conditions. The regulatory network was extracted from the PPI using the MCODE module present in Cytoscape. Our objective was to investigate the significance of hub genes in these diseases using gene ontology and KEGG pathway enrichment analysis. Methods: Our methodology involved collecting disease gene-related information from DisGeNET databases and performing protein-protein interaction (PPI) network and core genes screening. We then conducted gene ontology and KEGG pathway enrichment analysis. Results: We found that IL6 plays a critical role in all disease conditions and in different pathways that can be associated with the development of all four diseases. Conclusions: The theoretical importance of our research is that we employed various systems and structural biology techniques to identify a crucial protein that could serve as a promising target for treating multiple diseases. Our data collection and analysis procedures involved rigorous scrutiny, ensuring high-quality results. Our conclusion is that IL6 plays a significant role in all four diseases, and it can act as a potential target for treating them. Our findings may have important implications for the development of novel therapeutic interventions for these diseases. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=melanoma%20metastasis" title="melanoma metastasis">melanoma metastasis</a>, <a href="https://publications.waset.org/abstracts/search?q=rheumatoid%20arthritis" title=" rheumatoid arthritis"> rheumatoid arthritis</a>, <a href="https://publications.waset.org/abstracts/search?q=inflammatory%20bowel%20diseases" title=" inflammatory bowel diseases"> inflammatory bowel diseases</a>, <a href="https://publications.waset.org/abstracts/search?q=integrated%20bioinformatics%20analysis" title=" integrated bioinformatics analysis"> integrated bioinformatics analysis</a> </p> <a href="https://publications.waset.org/abstracts/168255/identification-of-significant-genes-in-rheumatoid-arthritis-melanoma-metastasis-ulcerative-colitis-and-crohns-disease" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/168255.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">89</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6150</span> Detecting Memory-Related Gene Modules in sc/snRNA-seq Data by Deep-Learning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yong%20Chen">Yong Chen</a> </p> <p class="card-text"><strong>Abstract:</strong></p> To understand the detailed molecular mechanisms of memory formation in engram cells is one of the most fundamental questions in neuroscience. Recent single-cell RNA-seq (scRNA-seq) and single-nucleus RNA-seq (snRNA-seq) techniques have allowed us to explore the sparsely activated engram ensembles, enabling access to the molecular mechanisms that underlie experience-dependent memory formation and consolidation. However, the absence of specific and powerful computational methods to detect memory-related genes (modules) and their regulatory relationships in the sc/snRNA-seq datasets has strictly limited the analysis of underlying mechanisms and memory coding principles in mammalian brains. Here, we present a deep-learning method named SCENTBOX, to detect memory-related gene modules and causal regulatory relationships among themfromsc/snRNA-seq datasets. SCENTBOX first constructs codifferential expression gene network (CEGN) from case versus control sc/snRNA-seq datasets. It then detects the highly correlated modules of differential expression genes (DEGs) in CEGN. The deep network embedding and attention-based convolutional neural network strategies are employed to precisely detect regulatory relationships among DEG genes in a module. We applied them on scRNA-seq datasets of TRAP; Ai14 mouse neurons with fear memory and detected not only known memory-related genes, but also the modules and potential causal regulations. Our results provided novel regulations within an interesting module, including Arc, Bdnf, Creb, Dusp1, Rgs4, and Btg2. Overall, our methods provide a general computational tool for processing sc/snRNA-seq data from case versus control studie and a systematic investigation of fear-memory-related gene modules. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=sc%2FsnRNA-seq" title="sc/snRNA-seq">sc/snRNA-seq</a>, <a href="https://publications.waset.org/abstracts/search?q=memory%20formation" title=" memory formation"> memory formation</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=gene%20module" title=" gene module"> gene module</a>, <a href="https://publications.waset.org/abstracts/search?q=causal%20inference" title=" causal inference"> causal inference</a> </p> <a href="https://publications.waset.org/abstracts/149019/detecting-memory-related-gene-modules-in-scsnrna-seq-data-by-deep-learning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/149019.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">120</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6149</span> Finding Bicluster on Gene Expression Data of Lymphoma Based on Singular Value Decomposition and Hierarchical Clustering</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Alhadi%20Bustaman">Alhadi Bustaman</a>, <a href="https://publications.waset.org/abstracts/search?q=Soeganda%20Formalidin"> Soeganda Formalidin</a>, <a href="https://publications.waset.org/abstracts/search?q=Titin%20Siswantining"> Titin Siswantining</a> </p> <p class="card-text"><strong>Abstract:</strong></p> DNA microarray technology is used to analyze thousand gene expression data simultaneously and a very important task for drug development and test, function annotation, and cancer diagnosis. Various clustering methods have been used for analyzing gene expression data. However, when analyzing very large and heterogeneous collections of gene expression data, conventional clustering methods often cannot produce a satisfactory solution. Biclustering algorithm has been used as an alternative approach to identifying structures from gene expression data. In this paper, we introduce a transform technique based on singular value decomposition to identify normalized matrix of gene expression data followed by Mixed-Clustering algorithm and the Lift algorithm, inspired in the node-deletion and node-addition phases proposed by Cheng and Church based on Agglomerative Hierarchical Clustering (AHC). Experimental study on standard datasets demonstrated the effectiveness of the algorithm in gene expression data. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=agglomerative%20hierarchical%20clustering%20%28AHC%29" title="agglomerative hierarchical clustering (AHC)">agglomerative hierarchical clustering (AHC)</a>, <a href="https://publications.waset.org/abstracts/search?q=biclustering" title=" biclustering"> biclustering</a>, <a href="https://publications.waset.org/abstracts/search?q=gene%20expression%20data" title=" gene expression data"> gene expression data</a>, <a href="https://publications.waset.org/abstracts/search?q=lymphoma" title=" lymphoma"> lymphoma</a>, <a href="https://publications.waset.org/abstracts/search?q=singular%20value%20decomposition%20%28SVD%29" title=" singular value decomposition (SVD)"> singular value decomposition (SVD)</a> </p> <a href="https://publications.waset.org/abstracts/72889/finding-bicluster-on-gene-expression-data-of-lymphoma-based-on-singular-value-decomposition-and-hierarchical-clustering" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/72889.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">278</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6148</span> Mutations in MTHFR Gene Associated with Mental Retardation and Cerebral Palsy Combined with Mental Retardation in Erbil City</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hazha%20Hidayat">Hazha Hidayat</a>, <a href="https://publications.waset.org/abstracts/search?q=Shayma%20Ibrahim"> Shayma Ibrahim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Folate metabolism plays a crucial role in the normal development of the neonatal central nervous system. It is regulated by MTHFR gene polymorphism. Any factors, which will affect this metabolism either by hereditary or gene mutation will lead to many mental disorders. The purpose of this study was to investigate whether MTHFR gene mutation contributes to the development of mental retardation and CP combined with mental retardation in Erbil city. DNA was isolated from the peripheral blood samples of 40 cases suffering from mental retardation (MR) and CP combined with MR were recruited, sequence the 4, 6, 7, 8 exons of the MTHFR gene were done to identify the variants. Exons were amplified by PCR technique and then sequenced according to Sanger method to show the differences with MTHFR reference sequences. We observed (14) mutations in 4, 6, 7, 8 exons in the MTHFR gene associated with Cerebral Palsy combined with mental retardation included deletion, insertion, Substitution. The current study provides additional evidence that multiple variations in the MTHFR gene are associated with mental retardation and Cerebral Palsy. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=methylenetetrahydrofolate%20reductase%20%28MTHFR%29%20gene" title="methylenetetrahydrofolate reductase (MTHFR) gene">methylenetetrahydrofolate reductase (MTHFR) gene</a>, <a href="https://publications.waset.org/abstracts/search?q=SNPs" title=" SNPs"> SNPs</a>, <a href="https://publications.waset.org/abstracts/search?q=homocysteine" title=" homocysteine"> homocysteine</a>, <a href="https://publications.waset.org/abstracts/search?q=sequencing" title=" sequencing"> sequencing</a> </p> <a href="https://publications.waset.org/abstracts/70927/mutations-in-mthfr-gene-associated-with-mental-retardation-and-cerebral-palsy-combined-with-mental-retardation-in-erbil-city" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/70927.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">308</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6147</span> Comparative Study on Daily Discharge Estimation of Soolegan River </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Redvan%20Ghasemlounia">Redvan Ghasemlounia</a>, <a href="https://publications.waset.org/abstracts/search?q=Elham%20Ansari"> Elham Ansari</a>, <a href="https://publications.waset.org/abstracts/search?q=Hikmet%20Kerem%20Cigizoglu"> Hikmet Kerem Cigizoglu </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Hydrological modeling in arid and semi-arid regions is very important. Iran has many regions with these climate conditions such as Chaharmahal and Bakhtiari province that needs lots of attention with an appropriate management. Forecasting of hydrological parameters and estimation of hydrological events of catchments, provide important information that used for design, management and operation of water resources such as river systems, and dams, widely. Discharge in rivers is one of these parameters. This study presents the application and comparison of some estimation methods such as Feed-Forward Back Propagation Neural Network (FFBPNN), Multi Linear Regression (MLR), Gene Expression Programming (GEP) and Bayesian Network (BN) to predict the daily flow discharge of the Soolegan River, located at Chaharmahal and Bakhtiari province, in Iran. In this study, Soolegan, station was considered. This Station is located in Soolegan River at 51° 14՜ Latitude 31° 38՜ longitude at North Karoon basin. The Soolegan station is 2086 meters higher than sea level. The data used in this study are daily discharge and daily precipitation of Soolegan station. Feed Forward Back Propagation Neural Network(FFBPNN), Multi Linear Regression (MLR), Gene Expression Programming (GEP) and Bayesian Network (BN) models were developed using the same input parameters for Soolegan's daily discharge estimation. The results of estimation models were compared with observed discharge values to evaluate performance of the developed models. Results of all methods were compared and shown in tables and charts. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ANN" title="ANN">ANN</a>, <a href="https://publications.waset.org/abstracts/search?q=multi%20linear%20regression" title=" multi linear regression"> multi linear regression</a>, <a href="https://publications.waset.org/abstracts/search?q=Bayesian%20network" title=" Bayesian network"> Bayesian network</a>, <a href="https://publications.waset.org/abstracts/search?q=forecasting" title=" forecasting"> forecasting</a>, <a href="https://publications.waset.org/abstracts/search?q=discharge" title=" discharge"> discharge</a>, <a href="https://publications.waset.org/abstracts/search?q=gene%20expression%20programming" title=" gene expression programming"> gene expression programming</a> </p> <a href="https://publications.waset.org/abstracts/18210/comparative-study-on-daily-discharge-estimation-of-soolegan-river" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/18210.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">561</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6146</span> A Review of Effective Gene Selection Methods for Cancer Classification Using Microarray Gene Expression Profile</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hala%20Alshamlan">Hala Alshamlan</a>, <a href="https://publications.waset.org/abstracts/search?q=Ghada%20Badr"> Ghada Badr</a>, <a href="https://publications.waset.org/abstracts/search?q=Yousef%20Alohali"> Yousef Alohali </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Cancer is one of the dreadful diseases, which causes considerable death rate in humans. DNA microarray-based gene expression profiling has been emerged as an efficient technique for cancer classification, as well as for diagnosis, prognosis, and treatment purposes. In recent years, a DNA microarray technique has gained more attraction in both scientific and in industrial fields. It is important to determine the informative genes that cause cancer to improve early cancer diagnosis and to give effective chemotherapy treatment. In order to gain deep insight into the cancer classification problem, it is necessary to take a closer look at the proposed gene selection methods. We believe that they should be an integral preprocessing step for cancer classification. Furthermore, finding an accurate gene selection method is a very significant issue in a cancer classification area because it reduces the dimensionality of microarray dataset and selects informative genes. In this paper, we classify and review the state-of-art gene selection methods. We proceed by evaluating the performance of each gene selection approach based on their classification accuracy and number of informative genes. In our evaluation, we will use four benchmark microarray datasets for the cancer diagnosis (leukemia, colon, lung, and prostate). In addition, we compare the performance of gene selection method to investigate the effective gene selection method that has the ability to identify a small set of marker genes, and ensure high cancer classification accuracy. To the best of our knowledge, this is the first attempt to compare gene selection approaches for cancer classification using microarray gene expression profile. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=gene%20selection" title="gene selection">gene selection</a>, <a href="https://publications.waset.org/abstracts/search?q=feature%20selection" title=" feature selection"> feature selection</a>, <a href="https://publications.waset.org/abstracts/search?q=cancer%20classification" title=" cancer classification"> cancer classification</a>, <a href="https://publications.waset.org/abstracts/search?q=microarray" title=" microarray"> microarray</a>, <a href="https://publications.waset.org/abstracts/search?q=gene%20expression%20profile" title=" gene expression profile"> gene expression profile</a> </p> <a href="https://publications.waset.org/abstracts/8991/a-review-of-effective-gene-selection-methods-for-cancer-classification-using-microarray-gene-expression-profile" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/8991.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">454</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6145</span> An Integrated Visualization Tool for Heat Map and Gene Ontology Graph</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Somyung%20Oh">Somyung Oh</a>, <a href="https://publications.waset.org/abstracts/search?q=Jeonghyeon%20Ha"> Jeonghyeon Ha</a>, <a href="https://publications.waset.org/abstracts/search?q=Kyungwon%20Lee"> Kyungwon Lee</a>, <a href="https://publications.waset.org/abstracts/search?q=Sejong%20Oh"> Sejong Oh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Microarray is a general scheme to find differentially expressed genes for target concept. The output is expressed by heat map, and biologists analyze related terms of gene ontology to find some characteristics of differentially expressed genes. In this paper, we propose integrated visualization tool for heat map and gene ontology graph. Previous two methods are used by static manner and separated way. Proposed visualization tool integrates them and users can interactively manage it. Users may easily find and confirm related terms of gene ontology for given differentially expressed genes. Proposed tool also visualize connections between genes on heat map and gene ontology graph. We expect biologists to find new meaningful topics by proposed tool. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=heat%20map" title="heat map">heat map</a>, <a href="https://publications.waset.org/abstracts/search?q=gene%20ontology" title=" gene ontology"> gene ontology</a>, <a href="https://publications.waset.org/abstracts/search?q=microarray" title=" microarray"> microarray</a>, <a href="https://publications.waset.org/abstracts/search?q=differentially%20expressed%20gene" title=" differentially expressed gene"> differentially expressed gene</a> </p> <a href="https://publications.waset.org/abstracts/49151/an-integrated-visualization-tool-for-heat-map-and-gene-ontology-graph" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/49151.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">316</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6144</span> Medical Neural Classifier Based on Improved Genetic Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Fadzil%20Ahmad">Fadzil Ahmad</a>, <a href="https://publications.waset.org/abstracts/search?q=Noor%20Ashidi%20Mat%20Isa"> Noor Ashidi Mat Isa</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study introduces an improved genetic algorithm procedure that focuses search around near optimal solution corresponded to a group of elite chromosome. This is achieved through a novel crossover technique known as Segmented Multi Chromosome Crossover. It preserves the highly important information contained in a gene segment of elite chromosome and allows an offspring to carry information from gene segment of multiple chromosomes. In this way the algorithm has better possibility to effectively explore the solution space. The improved GA is applied for the automatic and simultaneous parameter optimization and feature selection of artificial neural network in pattern recognition of medical problem, the cancer and diabetes disease. The experimental result shows that the average classification accuracy of the cancer and diabetes dataset has improved by 0.1% and 0.3% respectively using the new algorithm. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=genetic%20algorithm" title="genetic algorithm">genetic algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=artificial%20neural%20network" title=" artificial neural network"> artificial neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=pattern%20clasification" title=" pattern clasification"> pattern clasification</a>, <a href="https://publications.waset.org/abstracts/search?q=classification%20accuracy" title=" classification accuracy"> classification accuracy</a> </p> <a href="https://publications.waset.org/abstracts/14231/medical-neural-classifier-based-on-improved-genetic-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/14231.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">474</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6143</span> Application of KL Divergence for Estimation of Each Metabolic Pathway Genes</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shohei%20Maruyama">Shohei Maruyama</a>, <a href="https://publications.waset.org/abstracts/search?q=Yasuo%20Matsuyama"> Yasuo Matsuyama</a>, <a href="https://publications.waset.org/abstracts/search?q=Sachiyo%20Aburatani"> Sachiyo Aburatani</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The development of the method to annotate unknown gene functions is an important task in bioinformatics. One of the approaches for the annotation is The identification of the metabolic pathway that genes are involved in. Gene expression data have been utilized for the identification, since gene expression data reflect various intracellular phenomena. However, it has been difficult to estimate the gene function with high accuracy. It is considered that the low accuracy of the estimation is caused by the difficulty of accurately measuring a gene expression. Even though they are measured under the same condition, the gene expressions will vary usually. In this study, we proposed a feature extraction method focusing on the variability of gene expressions to estimate the genes' metabolic pathway accurately. First, we estimated the distribution of each gene expression from replicate data. Next, we calculated the similarity between all gene pairs by KL divergence, which is a method for calculating the similarity between distributions. Finally, we utilized the similarity vectors as feature vectors and trained the multiclass SVM for identifying the genes' metabolic pathway. To evaluate our developed method, we applied the method to budding yeast and trained the multiclass SVM for identifying the seven metabolic pathways. As a result, the accuracy that calculated by our developed method was higher than the one that calculated from the raw gene expression data. Thus, our developed method combined with KL divergence is useful for identifying the genes' metabolic pathway. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=metabolic%20pathways" title="metabolic pathways">metabolic pathways</a>, <a href="https://publications.waset.org/abstracts/search?q=gene%20expression%20data" title=" gene expression data"> gene expression data</a>, <a href="https://publications.waset.org/abstracts/search?q=microarray" title=" microarray"> microarray</a>, <a href="https://publications.waset.org/abstracts/search?q=Kullback%E2%80%93Leibler%20divergence" title=" Kullback–Leibler divergence"> Kullback–Leibler divergence</a>, <a href="https://publications.waset.org/abstracts/search?q=KL%20divergence" title=" KL divergence"> KL divergence</a>, <a href="https://publications.waset.org/abstracts/search?q=support%20vector%20machines" title=" support vector machines"> support vector machines</a>, <a href="https://publications.waset.org/abstracts/search?q=SVM" title=" SVM"> SVM</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a> </p> <a href="https://publications.waset.org/abstracts/23964/application-of-kl-divergence-for-estimation-of-each-metabolic-pathway-genes" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/23964.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">403</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6142</span> The Use of Medical Biotechnology to Treat Genetic Disease</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rachel%20Matar">Rachel Matar</a>, <a href="https://publications.waset.org/abstracts/search?q=Maxime%20Merheb"> Maxime Merheb</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Chemical drugs have been used for many centuries as the only way to cure diseases until the novel gene therapy has been created in 1960. Gene therapy is based on the insertion, correction, or inactivation of genes to treat people with genetic illness (1). Gene therapy has made wonders in Parkison’s, Alzheimer and multiple sclerosis. In addition to great promises in the healing of deadly diseases like many types of cancer and autoimmune diseases (2). This method implies the use of recombinant DNA technology with the help of different viral and non-viral vectors (3). It is nowadays used in somatic cells as well as embryos and gametes. Beside all the benefits of gene therapy, this technique is deemed by some opponents as an ethically unacceptable treatment as it implies playing with the genes of living organisms. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=gene%20therapy" title="gene therapy">gene therapy</a>, <a href="https://publications.waset.org/abstracts/search?q=genetic%20disease" title=" genetic disease"> genetic disease</a>, <a href="https://publications.waset.org/abstracts/search?q=cancer" title=" cancer"> cancer</a>, <a href="https://publications.waset.org/abstracts/search?q=multiple%20sclerosis" title=" multiple sclerosis"> multiple sclerosis</a> </p> <a href="https://publications.waset.org/abstracts/46593/the-use-of-medical-biotechnology-to-treat-genetic-disease" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/46593.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">541</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6141</span> PRKAG3 and RYR1 Gene in Latvian White Pigs</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Daina%20Jonkus">Daina Jonkus</a>, <a href="https://publications.waset.org/abstracts/search?q=Liga%20Paura"> Liga Paura</a>, <a href="https://publications.waset.org/abstracts/search?q=Tatjana%20Sjakste"> Tatjana Sjakste</a>, <a href="https://publications.waset.org/abstracts/search?q=Kristina%20Dokane"> Kristina Dokane</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The aim of this study was to analyse PRKAG3 and RYR1 gene and genotypes frequencies in Latvian White pigs’ breed. Genotypes of RYR1 gene two loci (rs196953058 and rs323041392) in 89 exon and PRKAG3 gene two loci (rs196958025 and rs344045190) in gene promoter were detected in 103 individuals of Latvian white pigs’ breed. Analysis of RYR1 gene loci rs196953058 shows all individuals are homozygous by T allele and all animals are with genotypes TT, its mean - in 2769 position is Phenylalanine. Analysis of RYR1 gene loci rs323041392 shows all individuals are homozygous by G allele and all animals are with genotypes GG, its mean - in 4119 positions is Asparagine. In loci rs196953058 and rs323041392, there were no gene polymorphisms. All analysed individuals by two loci rs196953058-rs323041392 have TT-GG genotypes or Phe-Asp amino acids. In PRKAG3 gene loci rs196958025 and rs344045190 there was gene polymorphisms. In both loci frequencies for A allele was higher: 84.6% for rs196958025 and 73.0% for rs344045190. Analysis of PRKAG3 gene loci rs196958025 shows 74% of individuals are homozygous by An allele and animals are with genotypes AA. Only 4% of individuals are homozygous by G allele and animals are with genotypes GG, which is associated with pale meat colour and higher drip loss. Analysis of PRKAG3 gene loci rs344045190 shows 46% of individuals are homozygous with genotypes AA and 54% of individuals are heterozygous with genotypes AG. There are no individuals with GG genotypes. According to the results, in Latvian white pigs population there are no rs344435545 (RYR1 gene) CT heterozygous or TT recessive homozygous genotypes, which is related to the meat quality and pigs’ stress syndrome; and there are 4% rs196958025 (PRKAG3 gene) GG recessive homozygote genotypes, which is related to the meat quality. Acknowledgment: the investigation is supported by VPP 2014-2017 AgroBioRes Project No. 3 LIVESTOCK. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=genotype%20frequencies" title="genotype frequencies">genotype frequencies</a>, <a href="https://publications.waset.org/abstracts/search?q=pig" title=" pig"> pig</a>, <a href="https://publications.waset.org/abstracts/search?q=PRKAG3" title=" PRKAG3"> PRKAG3</a>, <a href="https://publications.waset.org/abstracts/search?q=RYR1" title=" RYR1"> RYR1</a> </p> <a href="https://publications.waset.org/abstracts/59238/prkag3-and-ryr1-gene-in-latvian-white-pigs" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/59238.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">210</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6140</span> Bioinformatic Study of Follicle Stimulating Hormone Receptor (FSHR) Gene in Different Buffalo Breeds</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hamid%20Mustafa">Hamid Mustafa</a>, <a href="https://publications.waset.org/abstracts/search?q=Adeela%20Ajmal"> Adeela Ajmal</a>, <a href="https://publications.waset.org/abstracts/search?q=Kim%20EuiSoo"> Kim EuiSoo</a>, <a href="https://publications.waset.org/abstracts/search?q=Noor-ul-Ain"> Noor-ul-Ain</a> </p> <p class="card-text"><strong>Abstract:</strong></p> World wild, buffalo production is considered as most important component of food industry. Efficient buffalo production is related with reproductive performance of this species. Lack of knowledge of reproductive efficiency and its related genes in buffalo species is a major constraint for sustainable buffalo production. In this study, we performed some bioinformatics analysis on Follicle Stimulating Hormone Receptor (FSHR) gene and explored the possible relationship of this gene among different buffalo breeds and with other farm animals. We also found the evolution pattern for this gene among these species. We investigate CDS lengths, Stop codon variation, homology search, signal peptide, isoelectic point, tertiary structure, motifs and phylogenetic tree. The results of this study indicate 4 different motif in this gene, which are Activin-recp, GS motif, STYKc Protein kinase and transmembrane. The results also indicate that this gene has very close relationship with cattle, bison, sheep and goat. Multiple alignment (MA) showed high conservation of motif which indicates constancy of this gene during evolution. The results of this study can be used and applied for better understanding of this gene for better characterization of Follicle Stimulating Hormone Receptor (FSHR) gene structure in different farm animals, which would be helpful for efficient breeding plans for animal’s production. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=buffalo" title="buffalo">buffalo</a>, <a href="https://publications.waset.org/abstracts/search?q=FSHR%20gene" title=" FSHR gene"> FSHR gene</a>, <a href="https://publications.waset.org/abstracts/search?q=bioinformatics" title=" bioinformatics"> bioinformatics</a>, <a href="https://publications.waset.org/abstracts/search?q=production" title=" production "> production </a> </p> <a href="https://publications.waset.org/abstracts/22070/bioinformatic-study-of-follicle-stimulating-hormone-receptor-fshr-gene-in-different-buffalo-breeds" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/22070.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">532</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6139</span> PMEL Marker Identification of Dark and Light Feather Colours in Local Canary</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mudawamah%20Mudawamah">Mudawamah Mudawamah</a>, <a href="https://publications.waset.org/abstracts/search?q=Muhammad%20Z.%20Fadli"> Muhammad Z. Fadli</a>, <a href="https://publications.waset.org/abstracts/search?q=Gatot%20Ciptadi"> Gatot Ciptadi</a>, <a href="https://publications.waset.org/abstracts/search?q=Aulanni%E2%80%99am"> Aulanni’am</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Canary breeders have spread throughout Indonesian regions for the low-middle society and become an income source for them. The interesting phenomenon of the canary market is the feather colours become one of determining factor for the price. The advantages of this research were contributed to the molecular database as a base of selection and mating for the Indonesia canary breeder. The research method was experiment with the genome obtained from canary blood isolation. The genome did the PCR amplification with PMEL marker followed by sequencing. Canaries were used 24 heads of light and dark colour feathers. Research data analyses used BioEdit and Network 4.6.0.0 software. The results showed that all samples were amplification with PMEL gene with 500 bp fragment length. In base sequence of 40 was found Cytosine(C) in the light colour canaries, while the dark colour canaries was obtained Thymine (T) in same base sequence. Sequence results had 286-415 bp fragment and 10 haplotypes. The conclusions were the PMEL gene (gene of white pigment) was likely to be used PMEL gene to detect molecular genetic variation of dark and light colour feather. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=canary" title="canary">canary</a>, <a href="https://publications.waset.org/abstracts/search?q=haplotype" title=" haplotype"> haplotype</a>, <a href="https://publications.waset.org/abstracts/search?q=PMEL" title=" PMEL"> PMEL</a>, <a href="https://publications.waset.org/abstracts/search?q=sequence" title=" sequence"> sequence</a> </p> <a href="https://publications.waset.org/abstracts/39620/pmel-marker-identification-of-dark-and-light-feather-colours-in-local-canary" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/39620.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">237</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6138</span> Polymorphism of Candidate Genes for Meat Production in Lori Sheep </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shahram%20Nanekarania">Shahram Nanekarania</a>, <a href="https://publications.waset.org/abstracts/search?q=Majid%20Goodarzia"> Majid Goodarzia</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Calpastatin and callipyge have been known as one of the candidate genes in meat quality and quantity. Calpastatin gene has been located to chromosome 5 of sheep and callipyge gene has been localized in the telomeric region on ovine chromosome 18. The objective of this study was identification of calpastatin and callipyge genes polymorphism and analysis of genotype structure in population of Lori sheep kept in Iran. Blood samples were taken from 120 Lori sheep breed and genomic DNA was extracted by salting out method. Polymorphism was identified using the PCR-RFLP technique. The PCR products were digested with MspI and FaqI restriction enzymes for calpastatin gene and callipyge gene, respectively. In this population, three patterns were observed and AA, AB, BB genotype have been identified with the 0.32, 0.63, 0.05 frequencies for calpastatin gene. The results obtained for the callipyge gene revealed that only the wild-type allele A was observed, indicating that only genotype AA was present in the population under consideration. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=polymorphism" title="polymorphism">polymorphism</a>, <a href="https://publications.waset.org/abstracts/search?q=calpastatin" title=" calpastatin"> calpastatin</a>, <a href="https://publications.waset.org/abstracts/search?q=callipyge" title=" callipyge"> callipyge</a>, <a href="https://publications.waset.org/abstracts/search?q=PCR-RFLP" title=" PCR-RFLP"> PCR-RFLP</a>, <a href="https://publications.waset.org/abstracts/search?q=Lori%20sheep" title=" Lori sheep"> Lori sheep</a> </p> <a href="https://publications.waset.org/abstracts/8594/polymorphism-of-candidate-genes-for-meat-production-in-lori-sheep" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/8594.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">611</span> </span> </div> </div> <ul class="pagination"> <li class="page-item disabled"><span class="page-link">&lsaquo;</span></li> <li class="page-item active"><span class="page-link">1</span></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=gene%20network&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=gene%20network&page=3">3</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=gene%20network&page=4">4</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=gene%20network&page=5">5</a></li> <li class="page-item"><a class="page-link" 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