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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> 4489</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: differential gene expression</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4489</span> Pathway and Differential Gene Expression Studies for Colorectal Cancer</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ankita%20Shukla">Ankita Shukla</a>, <a href="https://publications.waset.org/abstracts/search?q=Tiratha%20Raj%20Singh"> Tiratha Raj Singh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Colorectal cancer (CRC) imposes serious mortality burden worldwide and it has been increasing for past consecutive years. Continuous efforts have been made so far to diagnose the disease condition and to identify the root cause for it. In this study, we performed the pathway level as well as the differential gene expression studies for CRC. We analyzed the gene expression profile GSE24514 from Gene Expression Omnibus (GEO) along with the gene pathways involved in the CRC. This analysis helps us to understand the behavior of the genes that have shown differential expression through their targeted pathways. Pathway analysis for the targeted genes covers the wider area which therefore decreases the possibility to miss the significant ones. This will prove to be beneficial to expose the ones that have not been given attention so far. Through this analysis, we attempt to understand the various neighboring genes that have close relationship to the targeted one and thus proved to be significantly controlling the CRC. It is anticipated that the identified hub and neighboring genes will provide new directions to look at the pathway level differently and will be crucial for the regulatory processes of the disease. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=mismatch%20repair" title="mismatch repair">mismatch repair</a>, <a href="https://publications.waset.org/abstracts/search?q=microsatellite%20instability" title=" microsatellite instability"> microsatellite instability</a>, <a href="https://publications.waset.org/abstracts/search?q=carcinogenesis" title=" carcinogenesis"> carcinogenesis</a>, <a href="https://publications.waset.org/abstracts/search?q=morbidity" title=" morbidity"> morbidity</a> </p> <a href="https://publications.waset.org/abstracts/63300/pathway-and-differential-gene-expression-studies-for-colorectal-cancer" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/63300.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">326</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">4488</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">274</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">4487</span> Correlation of P53 Gene Expression With Serum Alanine Transaminase Levels and Hepatitis B Viral Load in Cirrhosis and Hepatocellular Carcinoma Patients</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Umme%20Shahera">Umme Shahera</a>, <a href="https://publications.waset.org/abstracts/search?q=Saifullah%20Munshi"> Saifullah Munshi</a>, <a href="https://publications.waset.org/abstracts/search?q=Munira%20Jahan"> Munira Jahan</a>, <a href="https://publications.waset.org/abstracts/search?q=Afzalun%20Nessa"> Afzalun Nessa</a>, <a href="https://publications.waset.org/abstracts/search?q=Shahinul%20Alam"> Shahinul Alam</a>, <a href="https://publications.waset.org/abstracts/search?q=Shahina%20Tabassum"> Shahina Tabassum</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The development of HCC is a multi-stage process. Several extrinsic factors, such as aflatoxin, HBV, nutrition, alcohol, and trace elements are thought to initiate or/and promote the hepatocarcinogenesis. Alteration of p53 status is an important intrinsic factor in this process as p53 is essential for preventing inappropriate cell proliferation and maintaining genome integrity following genotoxic stress. This study was designed to assess the correlation of p53 gene expression with HBV-DNA and serum Alanine transaminase (ALT) in patients with cirrhosis and HCC. The study was conducted among 60 patients. The study population were divided into four groups (15 in each groups)-HBV positive cirrhosis, HBV negative cirrhosis, HBV positive HCC and HBV negative HCC. Expression of p53 gene was observed using real time PCR. P53 gene expressions in the above mentioned groups were correlated with serum ALT level and HBV viral load. p53 gene was significantly higher in HBV-positive patients with HCC than HBV-positive cirrhosis. Similarly, the expression of p53 was significantly higher in HBV-positive HCC than HBV-negative HCC patients. However, the expression of p53 was reduced in HBV-positive cirrhosis in comparison with HBV-negative cirrhosis. P53 gene expression in liver was not correlated with the serum levels of ALT in any of the study groups. HBV- DNA load also did not correlated with p53 gene expression in HBV positive HCC and HBV positive cirrhosis patients. This study shows that there was no significant change with the expression of p53 gene in any of the study groups with ALT level or viral load, though differential expression of p53 gene were observed in cirrhosis and HCC patients. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=P53" title="P53">P53</a>, <a href="https://publications.waset.org/abstracts/search?q=ALT" title=" ALT"> ALT</a>, <a href="https://publications.waset.org/abstracts/search?q=HBV-DNA" title=" HBV-DNA"> HBV-DNA</a>, <a href="https://publications.waset.org/abstracts/search?q=liver%20cirrhosis" title=" liver cirrhosis"> liver cirrhosis</a>, <a href="https://publications.waset.org/abstracts/search?q=hepatocellular%20carcinoma" title=" hepatocellular carcinoma"> hepatocellular carcinoma</a> </p> <a href="https://publications.waset.org/abstracts/157457/correlation-of-p53-gene-expression-with-serum-alanine-transaminase-levels-and-hepatitis-b-viral-load-in-cirrhosis-and-hepatocellular-carcinoma-patients" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/157457.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">102</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">4486</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">284</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">4485</span> Using Gene Expression Programming in Learning Process of Rough Neural Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sanaa%20Rashed%20Abdallah">Sanaa Rashed Abdallah</a>, <a href="https://publications.waset.org/abstracts/search?q=Yasser%20F.%20Hassan"> Yasser F. Hassan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The paper will introduce an approach where a rough sets, gene expression programming and rough neural networks are used cooperatively for learning and classification support. The Objective of gene expression programming rough neural networks (GEP-RNN) approach is to obtain new classified data with minimum error in training and testing process. Starting point of gene expression programming rough neural networks (GEP-RNN) approach is an information system and the output from this approach is a structure of rough neural networks which is including the weights and thresholds with minimum classification error. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=rough%20sets" title="rough sets">rough sets</a>, <a href="https://publications.waset.org/abstracts/search?q=gene%20expression%20programming" title=" gene expression programming"> gene expression programming</a>, <a href="https://publications.waset.org/abstracts/search?q=rough%20neural%20networks" title=" rough neural networks"> rough neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=classification" title=" classification"> classification</a> </p> <a href="https://publications.waset.org/abstracts/41805/using-gene-expression-programming-in-learning-process-of-rough-neural-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/41805.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">390</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">4484</span> Fruiting Body Specific Sc4 Hydrophobin Gene Plays a Role in Schizophyllum Commune Hyphal Attachment to Structured Glass Surfaces</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Evans%20Iyamu">Evans Iyamu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Genes encoding hydrophobins play distinct roles at different stages of the life cycle of fungi, and they foster hyphal attachment to surfaces. The hydrophobin Sc4 is known to provide a hydrophobic membrane lining of the gas channels within Schizophyllum commune fruiting bodies. Here, we cultivated non-fruiting, monokaryotic S. commune 12-43 on glass surfaces that could be verified by micrography. Differential gene expression profiling of nine hydrophobin genes and the hydrophobin-like sc15 gene by quantitative PCR showed significant up-regulation of sc4 when S. commune was attached to glass surfaces, also confirmed with RNA-Seq data analysis. Another silicate, namely quartz sand, was investigated, and induction of sc4 was seen as well. The up-regulation of the hydrophobin gene sc4 may indicate involvement in S. commune hyphal attachment to glass as well as quartz surfaces. We propose that the covering of hyphae by Sc4 allows for direct interaction with the hydrophobic surfaces of silicates and that differential functions of specific hydrophobin genes depend on the surface interface involved. This study could help with the clarification of the biological functions of hydrophobins in natural surroundings, including hydrophobic surface attachment. Therefore, the analysis of growth on glass serves as a basis for understanding S. commune interaction with glass surfaces while providing the possibility to visualize the interaction microscopically. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=hydrophobin" title="hydrophobin">hydrophobin</a>, <a href="https://publications.waset.org/abstracts/search?q=structured%20glass%20surfaces" title=" structured glass surfaces"> structured glass surfaces</a>, <a href="https://publications.waset.org/abstracts/search?q=differential%20gene%20expression" title=" differential gene expression"> differential gene expression</a>, <a href="https://publications.waset.org/abstracts/search?q=quartz%20sand" title=" quartz sand"> quartz sand</a> </p> <a href="https://publications.waset.org/abstracts/159087/fruiting-body-specific-sc4-hydrophobin-gene-plays-a-role-in-schizophyllum-commune-hyphal-attachment-to-structured-glass-surfaces" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/159087.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">129</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">4483</span> The Identification of Combined Genomic Expressions as a Diagnostic Factor for Oral Squamous Cell Carcinoma</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ki-Yeo%20Kim">Ki-Yeo Kim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Trends in genetics are transforming in order to identify differential coexpressions of correlated gene expression rather than the significant individual gene. Moreover, it is known that a combined biomarker pattern improves the discrimination of a specific cancer. The identification of the combined biomarker is also necessary for the early detection of invasive oral squamous cell carcinoma (OSCC). To identify the combined biomarker that could improve the discrimination of OSCC, we explored an appropriate number of genes in a combined gene set in order to attain the highest level of accuracy. After detecting a significant gene set, including the pre-defined number of genes, a combined expression was identified using the weights of genes in a gene set. We used the Principal Component Analysis (PCA) for the weight calculation. In this process, we used three public microarray datasets. One dataset was used for identifying the combined biomarker, and the other two datasets were used for validation. The discrimination accuracy was measured by the out-of-bag (OOB) error. There was no relation between the significance and the discrimination accuracy in each individual gene. The identified gene set included both significant and insignificant genes. One of the most significant gene sets in the classification of normal and OSCC included MMP1, SOCS3 and ACOX1. Furthermore, in the case of oral dysplasia and OSCC discrimination, two combined biomarkers were identified. The combined genomic expression achieved better performance in the discrimination of different conditions than in a single significant gene. Therefore, it could be expected that accurate diagnosis for cancer could be possible with a combined biomarker. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=oral%20squamous%20cell%20carcinoma" title="oral squamous cell carcinoma">oral squamous cell carcinoma</a>, <a href="https://publications.waset.org/abstracts/search?q=combined%20biomarker" title=" combined biomarker"> combined biomarker</a>, <a href="https://publications.waset.org/abstracts/search?q=microarray%20dataset" title=" microarray dataset"> microarray dataset</a>, <a href="https://publications.waset.org/abstracts/search?q=correlated%20genes" title=" correlated genes"> correlated genes</a> </p> <a href="https://publications.waset.org/abstracts/35990/the-identification-of-combined-genomic-expressions-as-a-diagnostic-factor-for-oral-squamous-cell-carcinoma" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/35990.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">427</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">4482</span> Innate Immune Expression in Heterophils in Response to LPS</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rohita%20Gupta">Rohita Gupta</a>, <a href="https://publications.waset.org/abstracts/search?q=G.%20S.%20Brah"> G. S. Brah</a>, <a href="https://publications.waset.org/abstracts/search?q=R.%20Verma"> R. Verma</a>, <a href="https://publications.waset.org/abstracts/search?q=C.%20S.%20Mukhopadhayay"> C. S. Mukhopadhayay</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Although chicken strains show differences in susceptibility to a number of diseases, the underlying immunological basis is yet to be elucidated. In the present study, heterophils were subjected to LPS stimulation and total RNA extraction, further differential gene expression was studied in broiler, layer and indigenous Aseel strain by Real Time RT-PCR at different time periods before and after induction. The expression of the 14 AvBDs and chTLR 1, 2, 3, 4, 5, 7, 15 and 21 was detectable in heterophils. The expression level of most of the AvBDs significantly increased (P<0.05) 3 hours post in vitro lipopolysaccharide challenge. Higher expression level and stronger activation of most AvBDs, NFkB-1 and IRF-3 in heterophils was observed with the stimulation of LPS in layer compared to broiler, and in Aseel compared to both layer and broiler. This investigation will allow more refined interpretation of immuno-genetic basis of the variable disease resistance/susceptibility in divergent stock of chicken including indigenous breed. Moreover, this study will be helpful in formulation of strategy for isolation of antimicrobial peptides from heterophils. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=differential%20expression" title="differential expression">differential expression</a>, <a href="https://publications.waset.org/abstracts/search?q=heterophils" title=" heterophils"> heterophils</a>, <a href="https://publications.waset.org/abstracts/search?q=cytokines" title=" cytokines"> cytokines</a>, <a href="https://publications.waset.org/abstracts/search?q=defensin" title=" defensin"> defensin</a>, <a href="https://publications.waset.org/abstracts/search?q=TLR" title=" TLR"> TLR</a> </p> <a href="https://publications.waset.org/abstracts/10174/innate-immune-expression-in-heterophils-in-response-to-lps" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/10174.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">500</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">4481</span> Transcriptomic Analyses of Kappaphycus alvarezii under Different Wavelengths of Light</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Vun%20Yee%20Thien">Vun Yee Thien</a>, <a href="https://publications.waset.org/abstracts/search?q=Kenneth%20Francis%20Rodrigues"> Kenneth Francis Rodrigues</a>, <a href="https://publications.waset.org/abstracts/search?q=Clemente%20Michael%20Vui%20Ling%20Wong"> Clemente Michael Vui Ling Wong</a>, <a href="https://publications.waset.org/abstracts/search?q=Wilson%20Thau%20Lym%20Yong"> Wilson Thau Lym Yong</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Transcriptomes associated with the process of photosynthesis have offered insights into the mechanism of gene regulation in terrestrial plants; however, limited information is available as far as macroalgae are concerned. This investigation aims to decipher the underlying mechanisms associated with photosynthesis in the red alga, Kappaphycus alvarezii, by performing a differential expression analysis on a de novo assembled transcriptomes. Comparative analysis of gene expression was designed to examine the alteration of light qualities and its effect on physiological mechanisms in the red alga. High-throughput paired-end RNA-sequencing was applied to profile the transcriptome of K. alvarezii irradiated with different wavelengths of light (blue 492-455 nm, green 577-492 nm and red 780-622 nm) as compared to the full light spectrum, resulted in more than 60 million reads individually and assembled using Trinity and SOAPdenovo-Trans. The transcripts were annotated in the NCBI non-redundant (nr) protein, SwissProt, KEGG and COG databases with a cutoff E-value of 1e-5 and nearly 30% of transcripts were assigned to functional annotation by Blast searches. Differential expression analysis was performed using edgeR. The DEGs were designated to six categories: BL (blue light) regulated, GL (green light) regulated, RL (red light) regulated, BL or GL regulated, BL or RL regulated, GL or RL regulated, and either BL, GL or RL regulated. These DEGs were mapped to terms in KEGG database and compared with the whole transcriptome background to search for genes that regulated by light quality. The outcomes of this study will enhance our understanding of molecular mechanisms underlying light-induced responses in red algae. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=de%20novo%20transcriptome%20sequencing" title="de novo transcriptome sequencing">de novo transcriptome sequencing</a>, <a href="https://publications.waset.org/abstracts/search?q=differential%20gene%20expression" title=" differential gene expression"> differential gene expression</a>, <a href="https://publications.waset.org/abstracts/search?q=Kappaphycus%20alvareziired" title=" Kappaphycus alvareziired"> Kappaphycus alvareziired</a>, <a href="https://publications.waset.org/abstracts/search?q=red%20alga" title=" red alga"> red alga</a> </p> <a href="https://publications.waset.org/abstracts/30147/transcriptomic-analyses-of-kappaphycus-alvarezii-under-different-wavelengths-of-light" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/30147.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">513</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">4480</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">409</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">4479</span> A Novel PfkB Gene Cloning and Characterization for Expression in Potato Plants</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Arfan%20Ali">Arfan Ali</a>, <a href="https://publications.waset.org/abstracts/search?q=Idrees%20Ahmad%20Nasir"> Idrees Ahmad Nasir</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Potato (Solanum tuberosum) is an important cash crop and popular vegetable in Pakistan and throughout the world. Cold storage of potatoes accelerates the conversion of starch into reduced sugars (glucose and fructose). This process causes dry mass and bitter taste in the potatoes that are not acceptable to end consumers. In the current study, the phosphofructokinase B gene was cloned into the pET-30 vector for protein expression and the pCambia-1301 vector for plant expression. Amplification of a 930bp product from an E. coli strain determined the successful isolation of the phosphofructokinase B gene. Restriction digestion using NcoI and BglII along with the amplification of the 930bp product using gene specific primers confirmed the successful cloning of the PfkB gene in both vectors. The protein was expressed as a His-PfkB fusion protein. Western blot analysis confirmed the presence of the 35 Kda PfkB protein when hybridized with anti-His antibodies. The construct Fani-01 was evaluated transiently using a histochemical gus assay. The appearance of blue color in the agroinfiltrated area of potato leaves confirmed the successful expression of construct Fani-01. Further, the area displaying gus expression was evaluated for PfkB expression using ELISA. Moreover, PfkB gene expression evaluated through transient expression determined successful gene expression and highlighted its potential utilization for stable expression in potato to reduce sweetening due to long-term storage. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=potato" title="potato">potato</a>, <a href="https://publications.waset.org/abstracts/search?q=Solanum%20tuberosum" title=" Solanum tuberosum"> Solanum tuberosum</a>, <a href="https://publications.waset.org/abstracts/search?q=transformation" title=" transformation"> transformation</a>, <a href="https://publications.waset.org/abstracts/search?q=PfkB" title=" PfkB"> PfkB</a>, <a href="https://publications.waset.org/abstracts/search?q=anti-sweetening" title=" anti-sweetening "> anti-sweetening </a> </p> <a href="https://publications.waset.org/abstracts/24921/a-novel-pfkb-gene-cloning-and-characterization-for-expression-in-potato-plants" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/24921.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">478</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">4478</span> Lipopolysaccharide Induced Avian Innate Immune Expression in Heterophils</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rohita%20Gupta">Rohita Gupta</a>, <a href="https://publications.waset.org/abstracts/search?q=G.%20S.%20Brah"> G. S. Brah</a>, <a href="https://publications.waset.org/abstracts/search?q=R.%20Verma"> R. Verma</a>, <a href="https://publications.waset.org/abstracts/search?q=C.%20S.%20Mukhopadhayay"> C. S. Mukhopadhayay</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Although chicken strains show differences in susceptibility to a number of diseases, the underlying immunological basis is yet to be elucidated. In the present study, heterophils were subjected to LPS stimulation and total RNA extraction, further differential gene expression was studied in broiler, layer and indigenous Aseel strain by Real Time RT-PCR at different time periods before and after induction. The expression of the 14 AvBDs and chTLR 1, 2, 3, 4, 5, 7, 15 and 21 was detectable in heterophils. The expression level of most of the AvBDs significantly increased (P<0.05) 3 hours post in vitro lipopolysaccharide challenge. Higher expression level and stronger activation of most AvBDs, NFkB-1 and IRF-3 in heterophils was observed, with the stimulation of LPS in layer compared to broiler, and in Aseel compared to both layer and broiler. This investigation will allow more refined interpretation of immuno-genetic basis of the variable disease resistance/susceptibility in divergent stock of chicken including indigenous breed. Moreover this study will be helpful in formulation of strategy for isolation of antimicrobial peptides from heterophils. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=differential%20expression" title="differential expression">differential expression</a>, <a href="https://publications.waset.org/abstracts/search?q=heterophils" title=" heterophils"> heterophils</a>, <a href="https://publications.waset.org/abstracts/search?q=cytokines" title=" cytokines"> cytokines</a>, <a href="https://publications.waset.org/abstracts/search?q=defensin" title=" defensin"> defensin</a>, <a href="https://publications.waset.org/abstracts/search?q=TLR" title=" TLR"> TLR</a> </p> <a href="https://publications.waset.org/abstracts/10002/lipopolysaccharide-induced-avian-innate-immune-expression-in-heterophils" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/10002.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">624</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">4477</span> Comparison between Effects of Free Curcumin and Curcumin Loaded NIPAAm-MAA Nanoparticles on Telomerase and Pinx1 Gene Expression in Lung Cancer Cells</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Y.%20Pilehvar-Soltanahmadi">Y. Pilehvar-Soltanahmadi</a>, <a href="https://publications.waset.org/abstracts/search?q=F.%20Badrzadeh"> F. Badrzadeh</a>, <a href="https://publications.waset.org/abstracts/search?q=N.%20Zarghami"> N. Zarghami</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Jalilzadeh-Tabrizi"> S. Jalilzadeh-Tabrizi</a>, <a href="https://publications.waset.org/abstracts/search?q=R.%20Zamani"> R. Zamani </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Herbal compounds such as curcumin which decrease telomerase and gene expression have been considered as beneficial tools for lung cancer treatment. In this article, we compared the effects of pure curcumin and curcumin-loaded NIPAAm-MAA nanoparticles on telomerase and PinX1 gene expression in a lung cancer cell line. A tetrazolium-based assay was used for determination of cytotoxic effects of curcumin on the Calu-6 lung cancer cell line and telomerase and pinX1 gene expression was measured with real-time PCR. MTT assay showed that Curcumin-loaded NIPAAm-MAA inhibited the growth of the Calu-6 lung cancer cell line in a time and dose-dependent manner. Our q-PCR results showed that the expression of telomerase gene was effectively reduced as the concentration of curcumin-loaded NIPAAm-MAA increased while expression of the PinX1 gene became elevated. The results showed that curcumin loaded NIPAAm-MAA exerted cytotoxic effects on the Calu-6 cell line through down-regulation of telomerase and stimulation of pinX1 gene expression. NIPPAm-MAA could be the good carrier for such kinds of hydrophobic agent. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=curcumin" title="curcumin">curcumin</a>, <a href="https://publications.waset.org/abstracts/search?q=NIPAAm-MAA" title=" NIPAAm-MAA"> NIPAAm-MAA</a>, <a href="https://publications.waset.org/abstracts/search?q=PinX1" title=" PinX1"> PinX1</a>, <a href="https://publications.waset.org/abstracts/search?q=telomerase" title=" telomerase"> telomerase</a>, <a href="https://publications.waset.org/abstracts/search?q=lung%20cancer%20cells" title=" lung cancer cells"> lung cancer cells</a> </p> <a href="https://publications.waset.org/abstracts/37740/comparison-between-effects-of-free-curcumin-and-curcumin-loaded-nipaam-maa-nanoparticles-on-telomerase-and-pinx1-gene-expression-in-lung-cancer-cells" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/37740.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">307</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">4476</span> Analysis of Expression Data Using Unsupervised Techniques</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20A.%20I%20Perera">M. A. I Perera</a>, <a href="https://publications.waset.org/abstracts/search?q=C.%20R.%20Wijesinghe"> C. R. Wijesinghe</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20R.%20%20Weerasinghe"> A. R. Weerasinghe</a> </p> <p class="card-text"><strong>Abstract:</strong></p> his study was conducted to review and identify the unsupervised techniques that can be employed to analyze gene expression data in order to identify better subtypes of tumors. Identifying subtypes of cancer help in improving the efficacy and reducing the toxicity of the treatments by identifying clues to find target therapeutics. Process of gene expression data analysis described under three steps as preprocessing, clustering, and cluster validation. Feature selection is important since the genomic data are high dimensional with a large number of features compared to samples. Hierarchical clustering and K Means are often used in the analysis of gene expression data. There are several cluster validation techniques used in validating the clusters. Heatmaps are an effective external validation method that allows comparing the identified classes with clinical variables and visual analysis of the classes. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cancer%20subtypes" title="cancer subtypes">cancer subtypes</a>, <a href="https://publications.waset.org/abstracts/search?q=gene%20expression%20data%20analysis" title=" gene expression data analysis"> gene expression data analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=clustering" title=" clustering"> clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=cluster%20validation" title=" cluster validation"> cluster validation</a> </p> <a href="https://publications.waset.org/abstracts/129027/analysis-of-expression-data-using-unsupervised-techniques" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/129027.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">153</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">4475</span> Wt1 and FoxL2 Genes Expression Pattern in Mesonephros-Gonad Complexes of Green Sea Turtle (Chelonia mydas) Embryos Incubated in Feminization and Masculinization Temperature</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Fitria%20D.%20Ayuningtyas">Fitria D. Ayuningtyas</a>, <a href="https://publications.waset.org/abstracts/search?q=Anggraini%20Barlian"> Anggraini Barlian</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Green turtle (Chelonia mydas) is one of TSD (Temperature-dependent Sex Determination, TSD) animals which sex is determined by the egg’s incubation temperature. GSD (Genotypic Sex Determination) homologous genes such as Wilms’ Tumor (Wt1) and Forkhead Box L2 (FoxL2) play a role in TSD animal sex determination process. Wt1 plays a role in both male pathway, as a transcription factor for Sf1 gene and in female pathway, as a transcription factor for Dax1. FoxL2 plays a role specifically in female sex determination, and known as transcriptional factor for Aromatase gene. Until now, research on the pattern of Wt1 and FoxL2 genes expression in C.mydas has not been conducted yet. The aim of this research is to know the pattern of Wt1 and FoxL2 genes expression in Mesonephros-Gonad (MG) complexes of Chelonia mydas embryos incubated in masculinizing temperature (MT) and feminizing temperature (FT). Eggs of C.mydas incubated in 3 different stage of TSP (Thermosensitive Period) at masculinizing temperature (26±10C, MT) and feminizing temperature (31±10C FT). Mesonefros-gonad complexes were isolated at Pre-TSP stage (FT at days 14th, MT at days 24th), TSP stage (FT at days 24th, MT at days 36th) and differentiated stage (FT at days 40th, MT at days 58th). RNA from mesonephros-gonad (MG) complexes were converted into cDNA by RT-PCR process, and the pattern of Wt1 and FoxL2 genes expression is analyzed by quantitative Real Time PCR (qPCR) method, β-actin gene is used as an internal control. The pattern of Wt1 gene expression in Pre-TSP stage was almost the same between MG complexes incubated at MT or FT, while TSP and differentiation stage, the pattern of Wt1 gene expression in MG complexes incubated at MT or FT was increased. Wt1 gene expression of MG complexes that incubated at FT was higher than at MT. There was a difference pattern between Wt1 gene expression in this research compared to the previous research in protein level. It could be assumed that the difference caused by post-transcriptional regulation mechanisms before mRNA of Wt1 gene translated into protein structure. The pattern of FoxL2 gene expression in Pre-TSP stage was almost the same between MG complexes that incubated at MT and FT, and increased in both TSP and differentiated stage. The FoxL2 gene expression in MG complexes that incubated in FT is higher than MT on TSP and differentiated stage. Based on the results of this research, it can be assumed that Wt1 and FoxL2 gene were expressed in MG complexes that incubated both at MT and FT since Pre-TSP stage. The pattern of Wt1 gene expression was increased in every stage of gonadal development, and so do the pattern of FoxL2 gene expression. Wt1 and FoxL2 gene expressions were higher in MG complexes incubated at FT than MT. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=chelonia%20mydas" title="chelonia mydas">chelonia mydas</a>, <a href="https://publications.waset.org/abstracts/search?q=FoxL2" title=" FoxL2"> FoxL2</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=TSD" title=" TSD"> TSD</a>, <a href="https://publications.waset.org/abstracts/search?q=Wt1" title=" Wt1"> Wt1</a> </p> <a href="https://publications.waset.org/abstracts/15175/wt1-and-foxl2-genes-expression-pattern-in-mesonephros-gonad-complexes-of-green-sea-turtle-chelonia-mydas-embryos-incubated-in-feminization-and-masculinization-temperature" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/15175.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">412</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">4474</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">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">4473</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">464</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">4472</span> lncRNA Gene Expression Profiling Analysis by TCGA RNA-Seq Data of Breast Cancer</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Xiaoping%20Su">Xiaoping Su</a>, <a href="https://publications.waset.org/abstracts/search?q=Gabriel%20G.%20Malouf"> Gabriel G. Malouf</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Introduction: Breast cancer is a heterogeneous disease that can be classified in 4 subgroups using transcriptional profiling. The role of lncRNA expression in human breast cancer biology, prognosis, and molecular classification remains unknown. Methods and results: Using an integrative comprehensive analysis of lncRNA, mRNA and DNA methylation in 900 breast cancer patients from The Cancer Genome Atlas (TCGA) project, we unraveled the molecular portraits of 1,700 expressed lncRNA. Some of those lncRNAs (i.e, HOTAIR) are previously reported and others are novel (i.e, HOTAIRM1, MAPT-AS1). The lncRNA classification correlated well with the PAM50 classification for basal-like, Her-2 enriched and luminal B subgroups, in contrast to the luminal A subgroup which behaved differently. Importantly, estrogen receptor (ESR1) expression was associated with distinct lncRNA networks in lncRNA clusters III and IV. Gene set enrichment analysis for cis- and trans-acting lncRNA showed enrichment for breast cancer signatures driven by breast cancer master regulators. Almost two third of those lncRNA were marked by enhancer chromatin modifications (i.e., H3K27ac), suggesting that lncRNA expression may result in increased activity of neighboring genes. Differential analysis of gene expression profiling data showed that lncRNA HOTAIRM1 was significantly down-regulated in basal-like subtype, and DNA methylation profiling data showed that lncRNA HOTAIRM1 was highly methylated in basal-like subtype. Thus, our integrative analysis of gene expression and DNA methylation strongly suggested that lncRNA HOTAIRM1 should be a tumor suppressor in basal-like subtype. Conclusion and significance: Our study depicts the first lncRNA molecular portrait of breast cancer and shows that lncRNA HOTAIRM1 might be a novel tumor suppressor. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=lncRNA%20profiling" title="lncRNA profiling">lncRNA profiling</a>, <a href="https://publications.waset.org/abstracts/search?q=breast%20cancer" title=" breast cancer"> breast cancer</a>, <a href="https://publications.waset.org/abstracts/search?q=HOTAIRM1" title=" HOTAIRM1"> HOTAIRM1</a>, <a href="https://publications.waset.org/abstracts/search?q=tumor%20suppressor" title=" tumor suppressor"> tumor suppressor</a> </p> <a href="https://publications.waset.org/abstracts/104472/lncrna-gene-expression-profiling-analysis-by-tcga-rna-seq-data-of-breast-cancer" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/104472.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">109</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">4471</span> Robustness Conditions for the Establishment of Stationary Patterns of Drosophila Segmentation Gene Expression</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ekaterina%20M.%20Myasnikova">Ekaterina M. Myasnikova</a>, <a href="https://publications.waset.org/abstracts/search?q=Andrey%20A.%20Makashov"> Andrey A. Makashov</a>, <a href="https://publications.waset.org/abstracts/search?q=Alexander%20V.%20Spirov"> Alexander V. Spirov</a> </p> <p class="card-text"><strong>Abstract:</strong></p> First manifestation of a segmentation pattern in the early Drosophila development is the formation of expression domains (along with the main embryo axis) of genes belonging to the trunk gene class. Highly variable expression of genes from gap family in early Drosophila embryo is strongly reduced by the start of gastrulation due to the gene cross-regulation. The dynamics of gene expression is described by a gene circuit model for a system of four gap genes. It is shown that for the formation of a steep and stationary border by the model it is necessary that there existed a nucleus (modeling point) in which the gene expression level is constant in time and hence is described by a stationary equation. All the rest genes expressed in this nucleus are in a dynamic equilibrium. The mechanism of border formation associated with the existence of a stationary nucleus is also confirmed by the experiment. An important advantage of this approach is that properties of the system in a stationary nucleus are described by algebraic equations and can be easily handled analytically. Thus we explicitly characterize the cross-regulation properties necessary for the robustness and formulate the conditions providing this effect through the properties of the initial input data. It is shown that our formally derived conditions are satisfied for the previously published model solutions. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=drosophila" title="drosophila">drosophila</a>, <a href="https://publications.waset.org/abstracts/search?q=gap%20genes" title=" gap genes"> gap genes</a>, <a href="https://publications.waset.org/abstracts/search?q=reaction-diffusion%20model" title=" reaction-diffusion model"> reaction-diffusion model</a>, <a href="https://publications.waset.org/abstracts/search?q=robustness" title=" robustness"> robustness</a> </p> <a href="https://publications.waset.org/abstracts/73794/robustness-conditions-for-the-establishment-of-stationary-patterns-of-drosophila-segmentation-gene-expression" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/73794.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">373</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">4470</span> Biophysically Motivated Phylogenies</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Catherine%20Felce">Catherine Felce</a>, <a href="https://publications.waset.org/abstracts/search?q=Lior%20Pachter"> Lior Pachter</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Current methods for building phylogenetic trees from gene expression data consider mean expression levels. With single-cell technologies, we can leverage more information about cell dynamics by considering the entire distribution of gene expression across cells. Using biophysical modeling, we propose a method for constructing phylogenetic trees from scRNA-seq data, building on Felsenstein's method of continuous characters. This method can highlight genes whose level of expression may be unchanged between species, but whose rates of transcription/decay may have evolved over time. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=phylogenetics" title="phylogenetics">phylogenetics</a>, <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=biophysical%20modeling" title=" biophysical modeling"> biophysical modeling</a>, <a href="https://publications.waset.org/abstracts/search?q=transcription" title=" transcription"> transcription</a> </p> <a href="https://publications.waset.org/abstracts/186016/biophysically-motivated-phylogenies" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/186016.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">65</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">4469</span> An Analysis on Clustering Based Gene Selection and Classification for Gene Expression Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=K.%20Sathishkumar">K. Sathishkumar</a>, <a href="https://publications.waset.org/abstracts/search?q=V.%20Thiagarasu">V. Thiagarasu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Due to recent advances in DNA microarray technology, it is now feasible to obtain gene expression profiles of tissue samples at relatively low costs. Many scientists around the world use the advantage of this gene profiling to characterize complex biological circumstances and diseases. Microarray techniques that are used in genome-wide gene expression and genome mutation analysis help scientists and physicians in understanding of the pathophysiological mechanisms, in diagnoses and prognoses, and choosing treatment plans. DNA microarray technology has now made it possible to simultaneously monitor the expression levels of thousands of genes during important biological processes and across collections of related samples. 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. A first step toward addressing this challenge is the use of clustering techniques, which is essential in the data mining process to reveal natural structures and identify interesting patterns in the underlying data. This work presents an analysis of several clustering algorithms proposed to deals with the gene expression data effectively. The existing clustering algorithms like Support Vector Machine (SVM), K-means algorithm and evolutionary algorithm etc. are analyzed thoroughly to identify the advantages and limitations. The performance evaluation of the existing algorithms is carried out to determine the best approach. In order to improve the classification performance of the best approach in terms of Accuracy, Convergence Behavior and processing time, a hybrid clustering based optimization approach has been proposed. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=microarray%20technology" title="microarray technology">microarray technology</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=clustering" title=" clustering"> clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=gene%20Selection" title=" gene Selection"> gene Selection</a> </p> <a href="https://publications.waset.org/abstracts/27523/an-analysis-on-clustering-based-gene-selection-and-classification-for-gene-expression-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/27523.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">330</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">4468</span> Assessing the Correlation Between miR-141 Expression, Common K-Ras Gene Mutations, and Their Impact on Prognosis in Colorectal Cancer Tissue of Iranian Patients</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shima%20Behzadi">Shima Behzadi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Context:MicroRNA expression and K-Ras gene mutations play crucial roles in the prognosis of colorectal cancer. Understanding their correlation can provide valuable insights for better diagnosis and treatment strategies. Methodology:A case-control study involving 100 Iranian patients diagnosed with colorectal cancer was conducted. The expression of miR-141 and the presence of K-Ras mutations were analyzed using real-time PCR. Findings:The study demonstrated a significant correlation between miR-141 expression and K-Ras gene mutations in colorectal cancer patients. Patients with K-Ras mutations exhibited higher levels of miR-141 expression, indicating its potential as a prognostic biomarker. Theoretical importance:This research highlights the potential utility of miR-141 as a prognostic marker in colorectal cancer, particularly in patients with K-Ras mutations. It contributes to the understanding of molecular mechanisms associated with colorectal cancer prognosis. Data collection:Tissue samples from colorectal cancer patients with and without K-Ras mutations were collected and analyzed for miR-141 expression levels. Analysis procedures:Real-time PCR was used to quantify miR-141 expression levels in tumor samples with K-Ras mutations compared to those without these mutations. Conclusion:The study confirms a significant association between miR-141 expression and K-Ras mutations in Iranian colorectal cancer patients, suggesting the potential of miR-141 as a valuable prognostic biomarker in this context. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=colorectal%20cancer" title="colorectal cancer">colorectal cancer</a>, <a href="https://publications.waset.org/abstracts/search?q=K-Ras%20gene" title=" K-Ras gene"> K-Ras gene</a>, <a href="https://publications.waset.org/abstracts/search?q=miR-141%20marker" title=" miR-141 marker"> miR-141 marker</a>, <a href="https://publications.waset.org/abstracts/search?q=real%20time%20PCR" title=" real time PCR"> real time PCR</a>, <a href="https://publications.waset.org/abstracts/search?q=electrophoresis" title=" electrophoresis"> electrophoresis</a> </p> <a href="https://publications.waset.org/abstracts/196449/assessing-the-correlation-between-mir-141-expression-common-k-ras-gene-mutations-and-their-impact-on-prognosis-in-colorectal-cancer-tissue-of-iranian-patients" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/196449.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">21</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">4467</span> Cloning and Expression of the ansZ Gene from Bacillus sp. CH11 Isolated from Chilca salterns in Peru</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Stephy%20Saavedra">Stephy Saavedra</a>, <a href="https://publications.waset.org/abstracts/search?q=Annsy%20C.%20Arredondo"> Annsy C. Arredondo</a>, <a href="https://publications.waset.org/abstracts/search?q=Gisele%20Monteiro"> Gisele Monteiro</a>, <a href="https://publications.waset.org/abstracts/search?q=Adalberto%20Pessoa%20Jr"> Adalberto Pessoa Jr</a>, <a href="https://publications.waset.org/abstracts/search?q=Carol%20N.%20Flores-Fernandez"> Carol N. Flores-Fernandez</a>, <a href="https://publications.waset.org/abstracts/search?q=Amparo%20I.%20Zavaleta"> Amparo I. Zavaleta</a> </p> <p class="card-text"><strong>Abstract:</strong></p> L-asparaginase from bacterial sources is used in leukemic treatment and food industry. This enzyme is classified based on its affinity towards L-asparagine and L-glutamine. Likewise, ansZ genes express L-asparaginase with higher affinity to L-asparagine. The aim of this work was to clone and express of ansZ gene from Bacillus sp. CH11 isolated from Chilca salterns in Peru. The gene encoding L-asparaginase was cloned into pET15b vector and transformed in Escherichia coli BL21 (DE3) pLysS. The expression was carried out in a batch culture using LB broth and 0.5 mM IPTG. The recombinant L-asparaginase showed a molecular weight of ~ 39 kDa by SDS PAGE and a specific activity of 3.19 IU/mg of protein. The cloning and expression of ansZ gene from this halotolerant Bacillus sp. CH11 allowed having a biological input to improve a future scaling-up. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ansZ%20gene" title="ansZ gene">ansZ gene</a>, <a href="https://publications.waset.org/abstracts/search?q=Bacillus%20sp" title=" Bacillus sp"> Bacillus sp</a>, <a href="https://publications.waset.org/abstracts/search?q=Chilca%20salterns" title=" Chilca salterns"> Chilca salterns</a>, <a href="https://publications.waset.org/abstracts/search?q=recombinant%20L-asparaginase" title=" recombinant L-asparaginase"> recombinant L-asparaginase</a> </p> <a href="https://publications.waset.org/abstracts/141113/cloning-and-expression-of-the-ansz-gene-from-bacillus-sp-ch11-isolated-from-chilca-salterns-in-peru" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/141113.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">186</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">4466</span> The Expression of Lipoprotein Lipase Gene with Fat Accumulations and Serum Biochemical Levels in Betong (KU Line) and Broiler Chickens</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=W.%20Loongyai">W. Loongyai</a>, <a href="https://publications.waset.org/abstracts/search?q=N.%20Saengsawang"> N. Saengsawang</a>, <a href="https://publications.waset.org/abstracts/search?q=W.%20Danvilai"> W. Danvilai</a>, <a href="https://publications.waset.org/abstracts/search?q=C.%20Kridtayopas"> C. Kridtayopas</a>, <a href="https://publications.waset.org/abstracts/search?q=P.%20Sopannarath"> P. Sopannarath</a>, <a href="https://publications.waset.org/abstracts/search?q=C.%20Bunchasak"> C. Bunchasak</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Betong chicken is a slow growing and a lean strain of chicken, while the rapid growth of broiler is accompanied by increased fat. We investigated the growth performance, fat accumulations, lipid serum biochemical levels and lipoprotein lipase (LPL) gene expression of female Betong (KU line) at the age of 4 and 6 weeks. A total of 80 female Betong chickens (KU line) and 80 female broiler chickens were reared under open system (each group had 4 replicates of 20 chicks per pen). The results showed that feed intake and average daily gain (ADG) of broiler chicken were significantly higher than Betong (KU line) (P < 0.01), while feed conversion ratio (FCR) of Betong (KU line) at week 6 were significantly lower than broiler chicken (P < 0.01) at 6 weeks. At 4 and 6 weeks, two birds per replicate were randomly selected and slaughtered. Carcass weight did not significantly differ between treatments; the percentage of abdominal fat and subcutaneous fat yield was higher in the broiler (P < 0.01) at 4 and 6 week. Total cholesterol and LDL level of broiler were higher than Betong (KU line) at 4 and 6 weeks (P < 0.05). Abdominal fat samples were collected for total RNA extraction. The cDNA was amplified using primers specific for LPL gene expression and analysed using real-time PCR. The results showed that the expression of LPL gene was not different when compared between Betong (KU line) and broiler chickens at the age of 4 and 6 weeks (P > 0.05). Our results indicated that broiler chickens had high growth rate and fat accumulation when compared with Betong (KU line) chickens, whereas LPL gene expression did not differ between breeds. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=lipoprotein%20lipase%20gene" title="lipoprotein lipase gene">lipoprotein lipase gene</a>, <a href="https://publications.waset.org/abstracts/search?q=Betong%20%28KU%20line%29" title=" Betong (KU line)"> Betong (KU line)</a>, <a href="https://publications.waset.org/abstracts/search?q=broiler" title=" broiler"> broiler</a>, <a href="https://publications.waset.org/abstracts/search?q=abdominal%20fat" title=" abdominal fat"> abdominal fat</a>, <a href="https://publications.waset.org/abstracts/search?q=gene%20expression" title=" gene expression"> gene expression</a> </p> <a href="https://publications.waset.org/abstracts/96914/the-expression-of-lipoprotein-lipase-gene-with-fat-accumulations-and-serum-biochemical-levels-in-betong-ku-line-and-broiler-chickens" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/96914.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">188</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">4465</span> Quantitative Evaluation of Endogenous Reference Genes for ddPCR under Salt Stress Using a Moderate Halophile</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Qinghua%20Xing">Qinghua Xing</a>, <a href="https://publications.waset.org/abstracts/search?q=Noha%20M.%20Mesbah"> Noha M. Mesbah</a>, <a href="https://publications.waset.org/abstracts/search?q=Haisheng%20Wang"> Haisheng Wang</a>, <a href="https://publications.waset.org/abstracts/search?q=Jun%20Li"> Jun Li</a>, <a href="https://publications.waset.org/abstracts/search?q=Baisuo%20Zhao"> Baisuo Zhao</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Droplet digital PCR (ddPCR) is being increasingly adopted for gene detection and quantification because of its higher sensitivity and specificity. According to previous observations and our lab data, it is essential to use endogenous reference genes (RGs) when investigating gene expression at the mRNA level under salt stress. This study aimed to select and validate suitable RGs for gene expression under salt stress using ddPCR. Six candidate RGs were selected based on the tandem mass tag (TMT)-labeled quantitative proteomics of Alkalicoccus halolimnae at four salinities. The expression stability of these candidate genes was evaluated using statistical algorithms (geNorm, NormFinder, BestKeeper and RefFinder). There was a small fluctuation in cycle threshold (Ct) value and copy number of the pdp gene. Its expression stability was ranked in the vanguard of all algorithms, and was the most suitable RG for quantification of expression by both qPCR and ddPCR of A. halolimnae under salt stress. Single RG pdp and RG combinations were used to normalize the expression of ectA, ectB, ectC, and ectD under four salinities. The present study constitutes the first systematic analysis of endogenous RG selection for halophiles responding to salt stress. This work provides a valuable theory and an approach reference of internal control identification for ddPCR-based stress response models. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=endogenous%20reference%20gene" title="endogenous reference gene">endogenous reference gene</a>, <a href="https://publications.waset.org/abstracts/search?q=salt%20stress" title=" salt stress"> salt stress</a>, <a href="https://publications.waset.org/abstracts/search?q=ddPCR" title=" ddPCR"> ddPCR</a>, <a href="https://publications.waset.org/abstracts/search?q=RT-qPCR" title=" RT-qPCR"> RT-qPCR</a>, <a href="https://publications.waset.org/abstracts/search?q=Alkalicoccus%20halolimnae" title=" Alkalicoccus halolimnae"> Alkalicoccus halolimnae</a> </p> <a href="https://publications.waset.org/abstracts/165112/quantitative-evaluation-of-endogenous-reference-genes-for-ddpcr-under-salt-stress-using-a-moderate-halophile" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/165112.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">111</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">4464</span> Investigation of FoxM1 Gene Expression in Breast Cancer and Its Relationship with miR-216b-5p Expression Level</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Neda%20Menbari">Neda Menbari</a>, <a href="https://publications.waset.org/abstracts/search?q=Ramin%20Mehdiabadi"> Ramin Mehdiabadi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Background: breast cancer remains a critical global health issue, constituting a leading cause of cancer-related mortality in women. MicroRNAs (miRs) are natural RNA molecules that play an important role in cellular processes and regulate post-transcriptional gene expression. MiR-216b-5p is a miR that acts as a tumor suppressor. The expression levels of FoxM1 and miR-216b-5p in malignant and control cells have been evaluated by quantitative polymerase chain reaction (qPCR) technique and flow cytometry. Results: the results of this study revealed a significant downregulation of miR-216b-5p in cancerous cells compared to the control MCF-10A cells (P=0.0004). Interestingly, the expression of miR-216b-5p exhibited an inverse relationship with key clinical indicators such as tumor size, grade, and lymph node invasion. Conclusion: The study's findings showed the prognostic value of miR-216b-5p levels in breast cancer, and its reduced expression correlates with unfavorable tumor characteristics. This research recommends performing more studies on the role of FoxM1 and miR-216b-5p in breast cancer pathology which potentially paving the way for targeted therapeutic interventions. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=breast%20cancer" title="breast cancer">breast cancer</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=FOXM1" title=" FOXM1"> FOXM1</a>, <a href="https://publications.waset.org/abstracts/search?q=microRNA" title=" microRNA"> microRNA</a> </p> <a href="https://publications.waset.org/abstracts/185448/investigation-of-foxm1-gene-expression-in-breast-cancer-and-its-relationship-with-mir-216b-5p-expression-level" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/185448.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">61</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">4463</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">103</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">4462</span> An Analysis System for Integrating High-Throughput Transcript Abundance Data with Metabolic Pathways in Green Algae</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Han-Qin%20Zheng">Han-Qin Zheng</a>, <a href="https://publications.waset.org/abstracts/search?q=Yi-Fan%20Chiang-Hsieh"> Yi-Fan Chiang-Hsieh</a>, <a href="https://publications.waset.org/abstracts/search?q=Chia-Hung%20Chien"> Chia-Hung Chien</a>, <a href="https://publications.waset.org/abstracts/search?q=Wen-Chi%20Chang"> Wen-Chi Chang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> As the most important non-vascular plants, algae have many research applications, including high species diversity, biofuel sources, adsorption of heavy metals and, following processing, health supplements. With the increasing availability of next-generation sequencing (NGS) data for algae genomes and transcriptomes, an integrated resource for retrieving gene expression data and metabolic pathway is essential for functional analysis and systems biology in algae. However, gene expression profiles and biological pathways are displayed separately in current resources, and making it impossible to search current databases directly to identify the cellular response mechanisms. Therefore, this work develops a novel AlgaePath database to retrieve gene expression profiles efficiently under various conditions in numerous metabolic pathways. AlgaePath, a web-based database, integrates gene information, biological pathways, and next-generation sequencing (NGS) datasets in Chlamydomonasreinhardtii and Neodesmus sp. UTEX 2219-4. Users can identify gene expression profiles and pathway information by using five query pages (i.e. Gene Search, Pathway Search, Differentially Expressed Genes (DEGs) Search, Gene Group Analysis, and Co-Expression Analysis). The gene expression data of 45 and 4 samples can be obtained directly on pathway maps in C. reinhardtii and Neodesmus sp. UTEX 2219-4, respectively. Genes that are differentially expressed between two conditions can be identified in Folds Search. Furthermore, the Gene Group Analysis of AlgaePath includes pathway enrichment analysis, and can easily compare the gene expression profiles of functionally related genes in a map. Finally, Co-Expression Analysis provides co-expressed transcripts of a target gene. The analysis results provide a valuable reference for designing further experiments and elucidating critical mechanisms from high-throughput data. More than an effective interface to clarify the transcript response mechanisms in different metabolic pathways under various conditions, AlgaePath is also a data mining system to identify critical mechanisms based on high-throughput sequencing. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=next-generation%20sequencing%20%28NGS%29" title="next-generation sequencing (NGS)">next-generation sequencing (NGS)</a>, <a href="https://publications.waset.org/abstracts/search?q=algae" title=" algae"> algae</a>, <a href="https://publications.waset.org/abstracts/search?q=transcriptome" title=" transcriptome"> transcriptome</a>, <a href="https://publications.waset.org/abstracts/search?q=metabolic%20pathway" title=" metabolic pathway"> metabolic pathway</a>, <a href="https://publications.waset.org/abstracts/search?q=co-expression" title=" co-expression"> co-expression</a> </p> <a href="https://publications.waset.org/abstracts/9022/an-analysis-system-for-integrating-high-throughput-transcript-abundance-data-with-metabolic-pathways-in-green-algae" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/9022.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">410</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">4461</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">247</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">4460</span> Construction of a Fusion Gene Carrying E10A and K5 with 2A Peptide-Linked by Using Overlap Extension PCR</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tiancheng%20Lan">Tiancheng Lan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> E10A is a kind of replication-defective adenovirus which carries the human endostatin gene to inhibit the growth of tumors. Kringle 5(K5) has almost the same function as angiostatin to also inhibit the growth of tumors since they are all the byproduct of the proteolytic cleavage of plasminogen. Tumor size increasing can be suppressed because both of the endostatin and K5 can restrain the angiogenesis process. Therefore, in order to improve the treatment effect on tumor, 2A peptide is used to construct a fusion gene carrying both E10A and K5. Using 2A peptide is an ideal strategy when a fusion gene is expressed because it can avoid many problems during the expression of more than one kind of protein. The overlap extension PCR is also used to connect 2A peptide with E10A and K5. The final construction of fusion gene E10A-2A-K5 can provide a possible new method of the anti-angiogenesis treatment with a better expression performance. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=E10A" title="E10A">E10A</a>, <a href="https://publications.waset.org/abstracts/search?q=Kringle%205" title=" Kringle 5"> Kringle 5</a>, <a href="https://publications.waset.org/abstracts/search?q=2A%20peptide" title=" 2A peptide"> 2A peptide</a>, <a href="https://publications.waset.org/abstracts/search?q=overlap%20extension%20PCR" title=" overlap extension PCR"> overlap extension PCR</a> </p> <a href="https://publications.waset.org/abstracts/132643/construction-of-a-fusion-gene-carrying-e10a-and-k5-with-2a-peptide-linked-by-using-overlap-extension-pcr" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/132643.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> <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=differential%20gene%20expression&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=differential%20gene%20expression&page=3">3</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=differential%20gene%20expression&page=4">4</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=differential%20gene%20expression&page=5">5</a></li> <li class="page-item"><a class="page-link" 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