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regulatory network</title> <meta name="description" content="Search results for: gene regulatory network"> <meta name="keywords" content="gene regulatory network"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button 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action="https://publications.waset.org/abstracts/search"> <div id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="gene regulatory network"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 6890</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: gene regulatory network</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6890</span> Construction of the Large Scale Biological Networks from Microarrays</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Fadhl%20Alakwaa">Fadhl Alakwaa</a> </p> <p class="card-text"><strong>Abstract:</strong></p> One of the sustainable goals of the system biology is understanding gene-gene interactions. Hence, gene regulatory networks (GRN) need to be constructed for understanding the disease ontology and to reduce the cost of drug development. To construct gene regulatory from gene expression we need to overcome many challenges such as data denoising and dimensionality. In this paper, we develop an integrated system to reduce data dimension and remove the noise. The generated network from our system was validated via available interaction databases and was compared to previous methods. The result revealed the performance of our proposed method. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=gene%20regulatory%20network" title="gene regulatory network">gene regulatory network</a>, <a href="https://publications.waset.org/abstracts/search?q=biclustering" title=" biclustering"> biclustering</a>, <a href="https://publications.waset.org/abstracts/search?q=denoising" title=" denoising"> denoising</a>, <a href="https://publications.waset.org/abstracts/search?q=system%20biology" title=" system biology"> system biology</a> </p> <a href="https://publications.waset.org/abstracts/74607/construction-of-the-large-scale-biological-networks-from-microarrays" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/74607.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">239</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6889</span> SCANet: A Workflow for Single-Cell Co-Expression Based Analysis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mhaned%20Oubounyt">Mhaned Oubounyt</a>, <a href="https://publications.waset.org/abstracts/search?q=Jan%20Baumbach"> Jan Baumbach</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Differences in co-expression networks between two or multiple cells (sub)types across conditions is a pressing problem in single-cell RNA sequencing (scRNA-seq). A key challenge is to define those co-variations that differ between or among cell types and/or conditions and phenotypes to examine small regulatory networks that can explain mechanistic differences. To this end, we developed SCANet, an all-in-one Python package that uses state-of-the-art algorithms to facilitate the workflow of a combined single-cell GCN (Gene Correlation Network) and GRN (Gene Regulatory Networks) pipeline, including inference of gene co-expression modules from scRNA-seq, followed by trait and cell type associations, hub gene detection, co-regulatory networks, and drug-gene interactions. In an example case, we illustrate how SCANet can be applied to identify regulatory drivers behind a cytokine storm associated with mortality in patients with acute respiratory illness. SCANet is available as a free, open-source, and user-friendly Python package that can be easily integrated into systems biology pipelines. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=single-cell" title="single-cell">single-cell</a>, <a href="https://publications.waset.org/abstracts/search?q=co-expression%20networks" title=" co-expression networks"> co-expression networks</a>, <a href="https://publications.waset.org/abstracts/search?q=drug-gene%20interactions" title=" drug-gene interactions"> drug-gene interactions</a>, <a href="https://publications.waset.org/abstracts/search?q=co-regulatory%20networks" title=" co-regulatory networks"> co-regulatory networks</a> </p> <a href="https://publications.waset.org/abstracts/161853/scanet-a-workflow-for-single-cell-co-expression-based-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/161853.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">150</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6888</span> Improved Predictive Models for the IRMA Network Using Nonlinear Optimisation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Vishwesh%20Kulkarni">Vishwesh Kulkarni</a>, <a href="https://publications.waset.org/abstracts/search?q=Nikhil%20Bellarykar"> Nikhil Bellarykar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Cellular complexity stems from the interactions among thousands of different molecular species. Thanks to the emerging fields of systems and synthetic biology, scientists are beginning to unravel these regulatory, signaling, and metabolic interactions and to understand their coordinated action. Reverse engineering of biological networks has has several benefits but a poor quality of data combined with the difficulty in reproducing it limits the applicability of these methods. A few years back, many of the commonly used predictive algorithms were tested on a network constructed in the yeast Saccharomyces cerevisiae (S. cerevisiae) to resolve this issue. The network was a synthetic network of five genes regulating each other for the so-called in vivo reverse-engineering and modeling assessment (IRMA). The network was constructed in S. cereviase since it is a simple and well characterized organism. The synthetic network included a variety of regulatory interactions, thus capturing the behaviour of larger eukaryotic gene networks on a smaller scale. We derive a new set of algorithms by solving a nonlinear optimization problem and show how these algorithms outperform other algorithms on these datasets. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=synthetic%20gene%20network" title="synthetic gene network">synthetic gene network</a>, <a href="https://publications.waset.org/abstracts/search?q=network%20identification" title=" network identification"> network identification</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=nonlinear%20modeling" title=" nonlinear modeling"> nonlinear modeling</a> </p> <a href="https://publications.waset.org/abstracts/94037/improved-predictive-models-for-the-irma-network-using-nonlinear-optimisation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/94037.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">156</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6887</span> Ordinary Differentiation Equations (ODE) Reconstruction of High-Dimensional Genetic Networks through Game Theory with Application to Dissecting Tree Salt Tolerance</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Libo%20Jiang">Libo Jiang</a>, <a href="https://publications.waset.org/abstracts/search?q=Huan%20Li"> Huan Li</a>, <a href="https://publications.waset.org/abstracts/search?q=Rongling%20Wu"> Rongling Wu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Ordinary differentiation equations (ODE) have proven to be powerful for reconstructing precise and informative gene regulatory networks (GRNs) from dynamic gene expression data. However, joint modeling and analysis of all genes, essential for the systematical characterization of genetic interactions, are challenging due to high dimensionality and a complex pattern of genetic regulation including activation, repression, and antitermination. Here, we address these challenges by unifying variable selection and game theory through ODE. Each gene within a GRN is co-expressed with its partner genes in a way like a game of multiple players, each of which tends to choose an optimal strategy to maximize its “fitness” across the whole network. Based on this unifying theory, we designed and conducted a real experiment to infer salt tolerance-related GRNs for Euphrates poplar, a hero tree that can grow in the saline desert. The pattern and magnitude of interactions between several hub genes within these GRNs were found to determine the capacity of Euphrates poplar to resist to saline stress. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=gene%20regulatory%20network" title="gene regulatory network">gene regulatory network</a>, <a href="https://publications.waset.org/abstracts/search?q=ordinary%20differential%20equation" title=" ordinary differential equation"> ordinary differential equation</a>, <a href="https://publications.waset.org/abstracts/search?q=game%20theory" title=" game theory"> game theory</a>, <a href="https://publications.waset.org/abstracts/search?q=LASSO" title=" LASSO"> LASSO</a>, <a href="https://publications.waset.org/abstracts/search?q=saline%20resistance" title=" saline resistance"> saline resistance</a> </p> <a href="https://publications.waset.org/abstracts/65286/ordinary-differentiation-equations-ode-reconstruction-of-high-dimensional-genetic-networks-through-game-theory-with-application-to-dissecting-tree-salt-tolerance" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/65286.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">639</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6886</span> Detecting Memory-Related Gene Modules in sc/snRNA-seq Data by Deep-Learning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yong%20Chen">Yong Chen</a> </p> <p class="card-text"><strong>Abstract:</strong></p> To understand the detailed molecular mechanisms of memory formation in engram cells is one of the most fundamental questions in neuroscience. Recent single-cell RNA-seq (scRNA-seq) and single-nucleus RNA-seq (snRNA-seq) techniques have allowed us to explore the sparsely activated engram ensembles, enabling access to the molecular mechanisms that underlie experience-dependent memory formation and consolidation. However, the absence of specific and powerful computational methods to detect memory-related genes (modules) and their regulatory relationships in the sc/snRNA-seq datasets has strictly limited the analysis of underlying mechanisms and memory coding principles in mammalian brains. Here, we present a deep-learning method named SCENTBOX, to detect memory-related gene modules and causal regulatory relationships among themfromsc/snRNA-seq datasets. SCENTBOX first constructs codifferential expression gene network (CEGN) from case versus control sc/snRNA-seq datasets. It then detects the highly correlated modules of differential expression genes (DEGs) in CEGN. The deep network embedding and attention-based convolutional neural network strategies are employed to precisely detect regulatory relationships among DEG genes in a module. We applied them on scRNA-seq datasets of TRAP; Ai14 mouse neurons with fear memory and detected not only known memory-related genes, but also the modules and potential causal regulations. Our results provided novel regulations within an interesting module, including Arc, Bdnf, Creb, Dusp1, Rgs4, and Btg2. Overall, our methods provide a general computational tool for processing sc/snRNA-seq data from case versus control studie and a systematic investigation of fear-memory-related gene modules. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=sc%2FsnRNA-seq" title="sc/snRNA-seq">sc/snRNA-seq</a>, <a href="https://publications.waset.org/abstracts/search?q=memory%20formation" title=" memory formation"> memory formation</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=gene%20module" title=" gene module"> gene module</a>, <a href="https://publications.waset.org/abstracts/search?q=causal%20inference" title=" causal inference"> causal inference</a> </p> <a href="https://publications.waset.org/abstracts/149019/detecting-memory-related-gene-modules-in-scsnrna-seq-data-by-deep-learning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/149019.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">120</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6885</span> Intra-miR-ExploreR, a Novel Bioinformatics Platform for Integrated Discovery of MiRNA:mRNA Gene Regulatory Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Surajit%20Bhattacharya">Surajit Bhattacharya</a>, <a href="https://publications.waset.org/abstracts/search?q=Daniel%20Veltri"> Daniel Veltri</a>, <a href="https://publications.waset.org/abstracts/search?q=Atit%20A.%20Patel"> Atit A. Patel</a>, <a href="https://publications.waset.org/abstracts/search?q=Daniel%20N.%20Cox"> Daniel N. Cox</a> </p> <p class="card-text"><strong>Abstract:</strong></p> miRNAs have emerged as key post-transcriptional regulators of gene expression, however identification of biologically-relevant target genes for this epigenetic regulatory mechanism remains a significant challenge. To address this knowledge gap, we have developed a novel tool in R, Intra-miR-ExploreR, that facilitates integrated discovery of miRNA targets by incorporating target databases and novel target prediction algorithms, using statistical methods including Pearson and Distance Correlation on microarray data, to arrive at high confidence intragenic miRNA target predictions. We have explored the efficacy of this tool using Drosophila melanogaster as a model organism for bioinformatics analyses and functional validation. A number of putative targets were obtained which were also validated using qRT-PCR analysis. Additional features of the tool include downloadable text files containing GO analysis from DAVID and Pubmed links of literature related to gene sets. Moreover, we are constructing interaction maps of intragenic miRNAs, using both micro array and RNA-seq data, focusing on neural tissues to uncover regulatory codes via which these molecules regulate gene expression to direct cellular development. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=miRNA" title="miRNA">miRNA</a>, <a href="https://publications.waset.org/abstracts/search?q=miRNA%3AmRNA%20target%20prediction" title=" miRNA:mRNA target prediction"> miRNA:mRNA target prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=statistical%20methods" title=" statistical methods"> statistical methods</a>, <a href="https://publications.waset.org/abstracts/search?q=miRNA%3AmRNA%20interaction%20network" title=" miRNA:mRNA interaction network"> miRNA:mRNA interaction network</a> </p> <a href="https://publications.waset.org/abstracts/27427/intra-mir-explorer-a-novel-bioinformatics-platform-for-integrated-discovery-of-mirnamrna-gene-regulatory-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/27427.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">510</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">6884</span> The Interplay between Autophagy and Macrophages' Polarization in Wound Healing: A Genetic Regulatory Network Analysis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mayada%20Mazher">Mayada Mazher</a>, <a href="https://publications.waset.org/abstracts/search?q=Ahmed%20Moustafa"> Ahmed Moustafa</a>, <a href="https://publications.waset.org/abstracts/search?q=Ahmed%20Abdellatif"> Ahmed Abdellatif</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Background: Autophagy is a eukaryotic, highly conserved catabolic process implicated in many pathophysiologies such as wound healing. Autophagy-associated genes serve as a scaffolding platform for signal transduction of macrophage polarization during the inflammatory phase of wound healing and tissue repair process. In the current study, we report a model for the interplay between autophagy-associated genes and macrophages polarization associated genes. Methods: In silico analysis was performed on 249 autophagy-related genes retrieved from the public autophagy database and gene expression data retrieved from Gene Expression Omnibus (GEO); GSE81922 and GSE69607 microarray data macrophages polarization 199 DEGS. An integrated protein-protein interaction network was constructed for autophagy and macrophage gene sets. The gene sets were then used for GO terms pathway enrichment analysis. Common transcription factors for autophagy and macrophages' polarization were identified. Finally, microRNAs enriched in both autophagy and macrophages were predicated. Results: In silico prediction of common transcription factors in DEGs macrophages and autophagy gene sets revealed a new role for the transcription factors, HOMEZ, GABPA, ELK1 and REL, that commonly regulate macrophages associated genes: IL6,IL1M, IL1B, NOS1, SOC3 and autophagy-related genes: Atg12, Rictor, Rb1cc1, Gaparab1, Atg16l1. Conclusions: Autophagy and macrophages' polarization are interdependent cellular processes, and both autophagy-related proteins and macrophages' polarization related proteins coordinate in tissue remodelling via transcription factors and microRNAs regulatory network. The current work highlights a potential new role for transcription factors HOMEZ, GABPA, ELK1 and REL in wound healing. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=autophagy%20related%20proteins" title="autophagy related proteins">autophagy related proteins</a>, <a href="https://publications.waset.org/abstracts/search?q=integrated%20network%20analysis" title=" integrated network analysis"> integrated network analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=macrophages%20polarization%20M1%20and%20M2" title=" macrophages polarization M1 and M2"> macrophages polarization M1 and M2</a>, <a href="https://publications.waset.org/abstracts/search?q=tissue%20remodelling" title=" tissue remodelling"> tissue remodelling</a> </p> <a href="https://publications.waset.org/abstracts/108802/the-interplay-between-autophagy-and-macrophages-polarization-in-wound-healing-a-genetic-regulatory-network-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/108802.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">152</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6883</span> ISMARA: Completely Automated Inference of Gene Regulatory Networks from High-Throughput Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Piotr%20J.%20Balwierz">Piotr J. Balwierz</a>, <a href="https://publications.waset.org/abstracts/search?q=Mikhail%20Pachkov"> Mikhail Pachkov</a>, <a href="https://publications.waset.org/abstracts/search?q=Phil%20Arnold"> Phil Arnold</a>, <a href="https://publications.waset.org/abstracts/search?q=Andreas%20J.%20Gruber"> Andreas J. Gruber</a>, <a href="https://publications.waset.org/abstracts/search?q=Mihaela%20Zavolan"> Mihaela Zavolan</a>, <a href="https://publications.waset.org/abstracts/search?q=Erik%20van%20Nimwegen"> Erik van Nimwegen</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Understanding the key players and interactions in the regulatory networks that control gene expression and chromatin state across different cell types and tissues in metazoans remains one of the central challenges in systems biology. Our laboratory has pioneered a number of methods for automatically inferring core gene regulatory networks directly from high-throughput data by modeling gene expression (RNA-seq) and chromatin state (ChIP-seq) measurements in terms of genome-wide computational predictions of regulatory sites for hundreds of transcription factors and micro-RNAs. These methods have now been completely automated in an integrated webserver called ISMARA that allows researchers to analyze their own data by simply uploading RNA-seq or ChIP-seq data sets and provides results in an integrated web interface as well as in downloadable flat form. For any data set, ISMARA infers the key regulators in the system, their activities across the input samples, the genes and pathways they target, and the core interactions between the regulators. We believe that by empowering experimental researchers to apply cutting-edge computational systems biology tools to their data in a completely automated manner, ISMARA can play an important role in developing our understanding of regulatory networks across metazoans. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=gene%20expression%20analysis" title="gene expression analysis">gene expression analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=high-throughput%20sequencing%20analysis" title=" high-throughput sequencing analysis"> high-throughput sequencing analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=transcription%20factor%20activity" title=" transcription factor activity"> transcription factor activity</a>, <a href="https://publications.waset.org/abstracts/search?q=transcription%20regulation" title=" transcription regulation"> transcription regulation</a> </p> <a href="https://publications.waset.org/abstracts/147333/ismara-completely-automated-inference-of-gene-regulatory-networks-from-high-throughput-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/147333.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">6882</span> Integration of Microarray Data into a Genome-Scale Metabolic Model to Study Flux Distribution after Gene Knockout</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mona%20Heydari">Mona Heydari</a>, <a href="https://publications.waset.org/abstracts/search?q=Ehsan%20Motamedian"> Ehsan Motamedian</a>, <a href="https://publications.waset.org/abstracts/search?q=Seyed%20Abbas%20Shojaosadati"> Seyed Abbas Shojaosadati</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Prediction of perturbations after genetic manipulation (especially gene knockout) is one of the important challenges in systems biology. In this paper, a new algorithm is introduced that integrates microarray data into the metabolic model. The algorithm was used to study the change in the cell phenotype after knockout of Gss gene in Escherichia coli BW25113. Algorithm implementation indicated that gene deletion resulted in more activation of the metabolic network. Growth yield was more and less regulating gene were identified for mutant in comparison with the wild-type strain. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=metabolic%20network" title="metabolic network">metabolic network</a>, <a href="https://publications.waset.org/abstracts/search?q=gene%20knockout" title=" gene knockout"> gene knockout</a>, <a href="https://publications.waset.org/abstracts/search?q=flux%20balance%20analysis" title=" flux balance analysis"> flux balance analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=microarray%20data" title=" microarray data"> microarray data</a>, <a href="https://publications.waset.org/abstracts/search?q=integration" title=" integration"> integration</a> </p> <a href="https://publications.waset.org/abstracts/15750/integration-of-microarray-data-into-a-genome-scale-metabolic-model-to-study-flux-distribution-after-gene-knockout" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/15750.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">579</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6881</span> Elucidation of the Sequential Transcriptional Activity in Escherichia coli Using Time-Series RNA-Seq Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Pui%20Shan%20Wong">Pui Shan Wong</a>, <a href="https://publications.waset.org/abstracts/search?q=Kosuke%20Tashiro"> Kosuke Tashiro</a>, <a href="https://publications.waset.org/abstracts/search?q=Satoru%20Kuhara"> Satoru Kuhara</a>, <a href="https://publications.waset.org/abstracts/search?q=Sachiyo%20Aburatani"> Sachiyo Aburatani</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Functional genomics and gene regulation inference has readily expanded our knowledge and understanding of gene interactions with regards to expression regulation. With the advancement of transcriptome sequencing in time-series comes the ability to study the sequential changes of the transcriptome. This method presented here works to augment existing regulation networks accumulated in literature with transcriptome data gathered from time-series experiments to construct a sequential representation of transcription factor activity. This method is applied on a time-series RNA-Seq data set from Escherichia coli as it transitions from growth to stationary phase over five hours. Investigations are conducted on the various metabolic activities in gene regulation processes by taking advantage of the correlation between regulatory gene pairs to examine their activity on a dynamic network. Especially, the changes in metabolic activity during phase transition are analyzed with focus on the pagP gene as well as other associated transcription factors. The visualization of the sequential transcriptional activity is used to describe the change in metabolic pathway activity originating from the pagP transcription factor, phoP. The results show a shift from amino acid and nucleic acid metabolism, to energy metabolism during the transition to stationary phase in E. coli. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Escherichia%20coli" title="Escherichia coli">Escherichia coli</a>, <a href="https://publications.waset.org/abstracts/search?q=gene%20regulation" title=" gene regulation"> gene regulation</a>, <a href="https://publications.waset.org/abstracts/search?q=network" title=" network"> network</a>, <a href="https://publications.waset.org/abstracts/search?q=time-series" title=" time-series"> time-series</a> </p> <a href="https://publications.waset.org/abstracts/65272/elucidation-of-the-sequential-transcriptional-activity-in-escherichia-coli-using-time-series-rna-seq-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/65272.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">372</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6880</span> Systematic Identification of Noncoding Cancer Driver Somatic Mutations</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Zohar%20Manber">Zohar Manber</a>, <a href="https://publications.waset.org/abstracts/search?q=Ran%20Elkon"> Ran Elkon</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Accumulation of somatic mutations (SMs) in the genome is a major driving force of cancer development. Most SMs in the tumor's genome are functionally neutral; however, some cause damage to critical processes and provide the tumor with a selective growth advantage (termed cancer driver mutations). Current research on functional significance of SMs is mainly focused on finding alterations in protein coding sequences. However, the exome comprises only 3% of the human genome, and thus, SMs in the noncoding genome significantly outnumber those that map to protein-coding regions. Although our understanding of noncoding driver SMs is very rudimentary, it is likely that disruption of regulatory elements in the genome is an important, yet largely underexplored mechanism by which somatic mutations contribute to cancer development. The expression of most human genes is controlled by multiple enhancers, and therefore, it is conceivable that regulatory SMs are distributed across different enhancers of the same target gene. Yet, to date, most statistical searches for regulatory SMs have considered each regulatory element individually, which may reduce statistical power. The first challenge in considering the cumulative activity of all the enhancers of a gene as a single unit is to map enhancers to their target promoters. Such mapping defines for each gene its set of regulating enhancers (termed "set of regulatory elements" (SRE)). Considering multiple enhancers of each gene as one unit holds great promise for enhancing the identification of driver regulatory SMs. However, the success of this approach is greatly dependent on the availability of comprehensive and accurate enhancer-promoter (E-P) maps. To date, the discovery of driver regulatory SMs has been hindered by insufficient sample sizes and statistical analyses that often considered each regulatory element separately. In this study, we analyzed more than 2,500 whole-genome sequence (WGS) samples provided by The Cancer Genome Atlas (TCGA) and The International Cancer Genome Consortium (ICGC) in order to identify such driver regulatory SMs. Our analyses took into account the combinatorial aspect of gene regulation by considering all the enhancers that control the same target gene as one unit, based on E-P maps from three genomics resources. The identification of candidate driver noncoding SMs is based on their recurrence. We searched for SREs of genes that are "hotspots" for SMs (that is, they accumulate SMs at a significantly elevated rate). To test the statistical significance of recurrence of SMs within a gene's SRE, we used both global and local background mutation rates. Using this approach, we detected - in seven different cancer types - numerous "hotspots" for SMs. To support the functional significance of these recurrent noncoding SMs, we further examined their association with the expression level of their target gene (using gene expression data provided by the ICGC and TCGA for samples that were also analyzed by WGS). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cancer%20genomics" title="cancer genomics">cancer genomics</a>, <a href="https://publications.waset.org/abstracts/search?q=enhancers" title=" enhancers"> enhancers</a>, <a href="https://publications.waset.org/abstracts/search?q=noncoding%20genome" title=" noncoding genome"> noncoding genome</a>, <a href="https://publications.waset.org/abstracts/search?q=regulatory%20elements" title=" regulatory elements "> regulatory elements </a> </p> <a href="https://publications.waset.org/abstracts/121517/systematic-identification-of-noncoding-cancer-driver-somatic-mutations" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/121517.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">104</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">6879</span> Target and Biomarker Identification Platform to Design New Drugs against Aging and Age-Related Diseases</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Peter%20Fedichev">Peter Fedichev</a> </p> <p class="card-text"><strong>Abstract:</strong></p> We studied fundamental aspects of aging to develop a mathematical model of gene regulatory network. We show that aging manifests itself as an inherent instability of gene network leading to exponential accumulation of regulatory errors with age. To validate our approach we studied age-dependent omic data such as transcriptomes, metabolomes etc. of different model organisms and humans. We build a computational platform based on our model to identify the targets and biomarkers of aging to design new drugs against aging and age-related diseases. As biomarkers of aging, we choose the rate of aging and the biological age since they completely determine the state of the organism. Since rate of aging rapidly changes in response to an external stress, this kind of biomarker can be useful as a tool for quantitative efficacy assessment of drugs, their combinations, dose optimization, chronic toxicity estimate, personalized therapies selection, clinical endpoints achievement (within clinical research), and death risk assessments. According to our model, we propose a method for targets identification for further interventions against aging and age-related diseases. Being a biotech company, we offer a complete pipeline to develop an anti-aging drug-candidate. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=aging" title="aging">aging</a>, <a href="https://publications.waset.org/abstracts/search?q=longevity" title=" longevity"> longevity</a>, <a href="https://publications.waset.org/abstracts/search?q=biomarkers" title=" biomarkers"> biomarkers</a>, <a href="https://publications.waset.org/abstracts/search?q=senescence" title=" senescence"> senescence</a> </p> <a href="https://publications.waset.org/abstracts/41675/target-and-biomarker-identification-platform-to-design-new-drugs-against-aging-and-age-related-diseases" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/41675.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">6878</span> Computational Model for Predicting Effective siRNA Sequences Using Whole Stacking Energy (ΔG) for Gene Silencing</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Reena%20Murali">Reena Murali</a>, <a href="https://publications.waset.org/abstracts/search?q=David%20Peter%20S."> David Peter S.</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The small interfering RNA (siRNA) alters the regulatory role of mRNA during gene expression by translational inhibition. Recent studies shows that up regulation of mRNA cause serious diseases like Cancer. So designing effective siRNA with good knockdown effects play an important role in gene silencing. Various siRNA design tools had been developed earlier. In this work, we are trying to analyze the existing good scoring second generation siRNA predicting tools and to optimize the efficiency of siRNA prediction by designing a computational model using Artificial Neural Network and whole stacking energy (ΔG), which may help in gene silencing and drug design in cancer therapy. Our model is trained and tested against a large data set of siRNA sequences. Validation of our results is done by finding correlation coefficient of experimental versus observed inhibition efficacy of siRNA. We achieved a correlation coefficient of 0.727 in our previous computational model and we could improve the correlation coefficient up to 0.753 when the threshold of whole tacking energy is greater than or equal to -32.5 kcal/mol. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=artificial%20neural%20network" title="artificial neural network">artificial neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=double%20stranded%20RNA" title=" double stranded RNA"> double stranded RNA</a>, <a href="https://publications.waset.org/abstracts/search?q=RNA%20interference" title=" RNA interference"> RNA interference</a>, <a href="https://publications.waset.org/abstracts/search?q=short%20interfering%20RNA" title=" short interfering RNA"> short interfering RNA</a> </p> <a href="https://publications.waset.org/abstracts/16841/computational-model-for-predicting-effective-sirna-sequences-using-whole-stacking-energy-dg-for-gene-silencing" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/16841.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">526</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6877</span> Gene Names Identity Recognition Using Siamese Network for Biomedical Publications</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Micheal%20Olaolu%20Arowolo">Micheal Olaolu Arowolo</a>, <a href="https://publications.waset.org/abstracts/search?q=Muhammad%20Azam"> Muhammad Azam</a>, <a href="https://publications.waset.org/abstracts/search?q=Fei%20He"> Fei He</a>, <a href="https://publications.waset.org/abstracts/search?q=Mihail%20Popescu"> Mihail Popescu</a>, <a href="https://publications.waset.org/abstracts/search?q=Dong%20Xu"> Dong Xu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> As the quantity of biological articles rises, so does the number of biological route figures. Each route figure shows gene names and relationships. Annotating pathway diagrams manually is time-consuming. Advanced image understanding models could speed up curation, but they must be more precise. There is rich information in biological pathway figures. The first step to performing image understanding of these figures is to recognize gene names automatically. Classical optical character recognition methods have been employed for gene name recognition, but they are not optimized for literature mining data. This study devised a method to recognize an image bounding box of gene name as a photo using deep Siamese neural network models to outperform the existing methods using ResNet, DenseNet and Inception architectures, the results obtained about 84% accuracy. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=biological%20pathway" title="biological pathway">biological pathway</a>, <a href="https://publications.waset.org/abstracts/search?q=gene%20identification" title=" gene identification"> gene identification</a>, <a href="https://publications.waset.org/abstracts/search?q=object%20detection" title=" object detection"> object detection</a>, <a href="https://publications.waset.org/abstracts/search?q=Siamese%20network" title=" Siamese network"> Siamese network</a> </p> <a href="https://publications.waset.org/abstracts/160725/gene-names-identity-recognition-using-siamese-network-for-biomedical-publications" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/160725.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">291</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6876</span> Bioinformatic Prediction of Hub Genes by Analysis of Signaling Pathways, Transcriptional Regulatory Networks and DNA Methylation Pattern in Colon Cancer</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ankan%20Roy">Ankan Roy</a>, <a href="https://publications.waset.org/abstracts/search?q=Niharika"> Niharika</a>, <a href="https://publications.waset.org/abstracts/search?q=Samir%20Kumar%20Patra"> Samir Kumar Patra</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Anomalous nexus of complex topological assemblies and spatiotemporal epigenetic choreography at chromosomal territory may forms the most sophisticated regulatory layer of gene expression in cancer. Colon cancer is one of the leading malignant neoplasms of the lower gastrointestinal tract worldwide. There is still a paucity of information about the complex molecular mechanisms of colonic cancerogenesis. Bioinformatics prediction and analysis helps to identify essential genes and significant pathways for monitoring and conquering this deadly disease. The present study investigates and explores potential hub genes as biomarkers and effective therapeutic targets for colon cancer treatment. Colon cancer patient sample containing gene expression profile datasets, such as GSE44076, GSE20916, and GSE37364 were downloaded from Gene Expression Omnibus (GEO) database and thoroughly screened using the GEO2R tool and Funrich software to find out common 2 differentially expressed genes (DEGs). Other approaches, including Gene Ontology (GO) and KEGG pathway analysis, Protein-Protein Interaction (PPI) network construction and hub gene investigation, Overall Survival (OS) analysis, gene correlation analysis, methylation pattern analysis, and hub gene-Transcription factors regulatory network construction, were performed and validated using various bioinformatics tool. Initially, we identified 166 DEGs, including 68 up-regulated and 98 down-regulated genes. Up-regulated genes are mainly associated with the Cytokine-cytokine receptor interaction, IL17 signaling pathway, ECM-receptor interaction, Focal adhesion and PI3K-Akt pathway. Downregulated genes are enriched in metabolic pathways, retinol metabolism, Steroid hormone biosynthesis, and bile secretion. From the protein-protein interaction network, thirty hub genes with high connectivity are selected using the MCODE and cytoHubba plugin. Survival analysis, expression validation, correlation analysis, and methylation pattern analysis were further verified using TCGA data. Finally, we predicted COL1A1, COL1A2, COL4A1, SPP1, SPARC, and THBS2 as potential master regulators in colonic cancerogenesis. Moreover, our experimental data highlights that disruption of lipid raft and RAS/MAPK signaling cascade affects this gene hub at mRNA level. We identified COL1A1, COL1A2, COL4A1, SPP1, SPARC, and THBS2 as determinant hub genes in colon cancer progression. They can be considered as biomarkers for diagnosis and promising therapeutic targets in colon cancer treatment. Additionally, our experimental data advertise that signaling pathway act as connecting link between membrane hub and gene hub. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=hub%20genes" title="hub genes">hub genes</a>, <a href="https://publications.waset.org/abstracts/search?q=colon%20cancer" title=" colon cancer"> colon cancer</a>, <a href="https://publications.waset.org/abstracts/search?q=DNA%20methylation" title=" DNA methylation"> DNA methylation</a>, <a href="https://publications.waset.org/abstracts/search?q=epigenetic%20engineering" title=" epigenetic engineering"> epigenetic engineering</a>, <a href="https://publications.waset.org/abstracts/search?q=bioinformatic%20predictions" title=" bioinformatic predictions"> bioinformatic predictions</a> </p> <a href="https://publications.waset.org/abstracts/152788/bioinformatic-prediction-of-hub-genes-by-analysis-of-signaling-pathways-transcriptional-regulatory-networks-and-dna-methylation-pattern-in-colon-cancer" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/152788.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">128</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">6875</span> The Need for a Consistent Regulatory Framework for CRISPR Gene-Editing in the European Union</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Andrew%20Thayer">Andrew Thayer</a>, <a href="https://publications.waset.org/abstracts/search?q=Courtney%20Rondeau"> Courtney Rondeau</a>, <a href="https://publications.waset.org/abstracts/search?q=Paraskevi%20Papadopoulou"> Paraskevi Papadopoulou</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) gene-editing technologies have generated considerable discussion about the applications and ethics of their use. However, no consistent guidelines for using CRISPR technologies have been developed -nor common legislation passed related to gene editing, especially as it is connected to genetically modified organisms (GMOs) in the European Union. The recent announcement that the first babies with CRISPR-edited genes were born, along with new studies exploring CRISPR’s applications in treating thalassemia, sickle-cell anemia, cancer, and certain forms of blindness, have demonstrated that the technology is developing faster than the policies needed to control it. Therefore, it can be seen that a reasonable and coherent regulatory framework for the use of CRISPR in human somatic and germline cells is necessary to ensure the ethical use of the technology in future years. The European Union serves as a unique region of interconnected countries without a standard set of regulations or legislation for CRISPR gene-editing. We posit that the EU would serve as a suitable model in comparing the legislations of its affiliated countries in order to understand the practicality and effectiveness of adopting majority-approved practices. Additionally, we present a proposed set of guidelines which could serve as a basis in developing a consistent regulatory framework for the EU countries to implement but also act as a good example for other countries to adhere to. Finally, an additional, multidimensional framework of smart solutions is proposed with which all stakeholders are engaged to become better-informed citizens. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=CRISPR" title="CRISPR">CRISPR</a>, <a href="https://publications.waset.org/abstracts/search?q=ethics" title=" ethics"> ethics</a>, <a href="https://publications.waset.org/abstracts/search?q=regulatory%20framework" title=" regulatory framework"> regulatory framework</a>, <a href="https://publications.waset.org/abstracts/search?q=European%20legislation" title=" European legislation"> European legislation</a> </p> <a href="https://publications.waset.org/abstracts/118942/the-need-for-a-consistent-regulatory-framework-for-crispr-gene-editing-in-the-european-union" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/118942.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">135</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">6874</span> Recognition of Gene Names from Gene Pathway Figures Using Siamese Network</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Muhammad%20Azam">Muhammad Azam</a>, <a href="https://publications.waset.org/abstracts/search?q=Micheal%20Olaolu%20Arowolo"> Micheal Olaolu Arowolo</a>, <a href="https://publications.waset.org/abstracts/search?q=Fei%20He"> Fei He</a>, <a href="https://publications.waset.org/abstracts/search?q=Mihail%20Popescu"> Mihail Popescu</a>, <a href="https://publications.waset.org/abstracts/search?q=Dong%20Xu"> Dong Xu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The number of biological papers is growing quickly, which means that the number of biological pathway figures in those papers is also increasing quickly. Each pathway figure shows extensive biological information, like the names of genes and how the genes are related. However, manually annotating pathway figures takes a lot of time and work. Even though using advanced image understanding models could speed up the process of curation, these models still need to be made more accurate. To improve gene name recognition from pathway figures, we applied a Siamese network to map image segments to a library of pictures containing known genes in a similar way to person recognition from photos in many photo applications. We used a triple loss function and a triplet spatial pyramid pooling network by combining the triplet convolution neural network and the spatial pyramid pooling (TSPP-Net). We compared VGG19 and VGG16 as the Siamese network model. VGG16 achieved better performance with an accuracy of 93%, which is much higher than OCR results. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=biological%20pathway" title="biological pathway">biological pathway</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20understanding" title=" image understanding"> image understanding</a>, <a href="https://publications.waset.org/abstracts/search?q=gene%20name%20recognition" title=" gene name recognition"> gene name recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=object%20detection" title=" object detection"> object detection</a>, <a href="https://publications.waset.org/abstracts/search?q=Siamese%20network" title=" Siamese network"> Siamese network</a>, <a href="https://publications.waset.org/abstracts/search?q=VGG" title=" VGG"> VGG</a> </p> <a href="https://publications.waset.org/abstracts/160723/recognition-of-gene-names-from-gene-pathway-figures-using-siamese-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/160723.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">291</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6873</span> Identification of Significant Genes in Rheumatoid Arthritis, Melanoma Metastasis, Ulcerative Colitis and Crohn’s Disease</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Krishna%20Pal%20Singh">Krishna Pal Singh</a>, <a href="https://publications.waset.org/abstracts/search?q=Shailendra%20Kumar%20Gupta"> Shailendra Kumar Gupta</a>, <a href="https://publications.waset.org/abstracts/search?q=Olaf%20Wolkenhauer"> Olaf Wolkenhauer</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Background: Our study aimed to identify common genes and potential targets across the four diseases, which include rheumatoid arthritis, melanoma metastasis, ulcerative colitis, and Crohn’s disease. We used a network and systems biology approach to identify the hub gene, which can act as a potential target for all four disease conditions. The regulatory network was extracted from the PPI using the MCODE module present in Cytoscape. Our objective was to investigate the significance of hub genes in these diseases using gene ontology and KEGG pathway enrichment analysis. Methods: Our methodology involved collecting disease gene-related information from DisGeNET databases and performing protein-protein interaction (PPI) network and core genes screening. We then conducted gene ontology and KEGG pathway enrichment analysis. Results: We found that IL6 plays a critical role in all disease conditions and in different pathways that can be associated with the development of all four diseases. Conclusions: The theoretical importance of our research is that we employed various systems and structural biology techniques to identify a crucial protein that could serve as a promising target for treating multiple diseases. Our data collection and analysis procedures involved rigorous scrutiny, ensuring high-quality results. Our conclusion is that IL6 plays a significant role in all four diseases, and it can act as a potential target for treating them. Our findings may have important implications for the development of novel therapeutic interventions for these diseases. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=melanoma%20metastasis" title="melanoma metastasis">melanoma metastasis</a>, <a href="https://publications.waset.org/abstracts/search?q=rheumatoid%20arthritis" title=" rheumatoid arthritis"> rheumatoid arthritis</a>, <a href="https://publications.waset.org/abstracts/search?q=inflammatory%20bowel%20diseases" title=" inflammatory bowel diseases"> inflammatory bowel diseases</a>, <a href="https://publications.waset.org/abstracts/search?q=integrated%20bioinformatics%20analysis" title=" integrated bioinformatics analysis"> integrated bioinformatics analysis</a> </p> <a href="https://publications.waset.org/abstracts/168255/identification-of-significant-genes-in-rheumatoid-arthritis-melanoma-metastasis-ulcerative-colitis-and-crohns-disease" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/168255.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">89</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6872</span> Regularization of Gene Regulatory Networks Perturbed by White Noise</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ramazan%20I.%20Kadiev">Ramazan I. Kadiev</a>, <a href="https://publications.waset.org/abstracts/search?q=Arcady%20Ponosov"> Arcady Ponosov</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Mathematical models of gene regulatory networks can in many cases be described by ordinary differential equations with switching nonlinearities, where the initial value problem is ill-posed. Several regularization methods are known in the case of deterministic networks, but the presence of stochastic noise leads to several technical difficulties. In the presentation, it is proposed to apply the methods of the stochastic singular perturbation theory going back to Yu. Kabanov and Yu. Pergamentshchikov. This approach is used to regularize the above ill-posed problem, which, e.g., makes it possible to design stable numerical schemes. Several examples are provided in the presentation, which support the efficiency of the suggested analysis. The method can also be of interest in other fields of biomathematics, where differential equations contain switchings, e.g., in neural field models. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ill-posed%20problems" title="ill-posed problems">ill-posed problems</a>, <a href="https://publications.waset.org/abstracts/search?q=singular%20perturbation%20analysis" title=" singular perturbation analysis"> singular perturbation analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=stochastic%20differential%20equations" title=" stochastic differential equations"> stochastic differential equations</a>, <a href="https://publications.waset.org/abstracts/search?q=switching%20nonlinearities" title=" switching nonlinearities"> switching nonlinearities</a> </p> <a href="https://publications.waset.org/abstracts/85883/regularization-of-gene-regulatory-networks-perturbed-by-white-noise" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/85883.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">194</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">6871</span> New Active Dioxin Response Element Sites in Regulatory Region of Human and Viral Genes</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ilya%20B.%20Tsyrlov">Ilya B. Tsyrlov</a>, <a href="https://publications.waset.org/abstracts/search?q=Dmitry%20Y.%20Oshchepkov"> Dmitry Y. Oshchepkov</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A computational search for dioxin response elements (DREs) in genes of proteins comprising the Ah receptor (AhR) cytosolic core complex was performed by highly efficient tool SITECON. Eventually, the following number of new DREs in 5’flanking region was detected by SITECON: one in AHR gene, five in XAP2, eight in HSP90AA1, and three in HSP90AB1 genes. Numerous DREs found in genes of AhR and AhR cytosolic complex members would shed a light on potential mechanisms of expression, the stoichiometry of unliganded AhR core complex, and its degradation vs biosynthesis dynamics resulted from treatment of target cells with the AhR most potent ligand, 2,3,7,8-TCDD. With human viruses, reduced susceptibility to TCDD of geneencoding HIV-1 P247 was justified by the only potential DRE determined in gag gene encoding HIV-1 P24 protein, whereas the regulatory region of CMV genes encoding IE gp/UL37 has five potent DRE, 1.65 kb/UL36 – six DRE, pp65 and pp71 – each has seven DRE, and pp150 – ten DRE. Also, from six to eight DRE were determined with SITECON in the regulatory region of HSV-1 IE genes encoding tegument proteins, UL36 and UL37, and of UL19 gene encoding bindingglycoprotein C (gC). So, TCDD in the low picomolar range may activate in human cells AhR: Arnt transcription pathway that triggers CMV and HSV-1 reactivation by binding to numerous promoter DRE within immediate-early (IE) genes UL37 and UL36, thus committing virus to the lytic cycle. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=dioxin%20response%20elements" title="dioxin response elements">dioxin response elements</a>, <a href="https://publications.waset.org/abstracts/search?q=Ah%20receptor" title=" Ah receptor"> Ah receptor</a>, <a href="https://publications.waset.org/abstracts/search?q=AhR%3A%20Arnt%20transcription%20pathway" title=" AhR: Arnt transcription pathway"> AhR: Arnt transcription pathway</a>, <a href="https://publications.waset.org/abstracts/search?q=human%20and%20viral%20genes" title=" human and viral genes"> human and viral genes</a> </p> <a href="https://publications.waset.org/abstracts/150381/new-active-dioxin-response-element-sites-in-regulatory-region-of-human-and-viral-genes" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/150381.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">104</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">6870</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">320</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">6869</span> Characterization of (GRAS37) Gibberellin Acid Insensitive (GAI), Repressor (RGA), and Scarecrow (SCR) Gene by Using Bioinformatics Tools</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yusra%20Tariq">Yusra Tariq</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The Grass 37 gene is presently known in tomatoes, which are the source of healthy substances such as ascorbic acid, polyphenols, carotenoids and nutrients. It has a significant impact on the growth and development of humans. The GRASS 37 gene is a plant Transcription factor group assuming significant parts in various reactions of different Abiotic stresses such as (drought, salinity, thermal stresses, temperature, and bright waves) which could highly affect the growth. Tomatoes are very sensitive to temperature, and their growth or production occurs optimally in a temperature range from 21 C to 29.5 C during the daytime and from 18.5 C to 21 C during the night. This protein acts as a positive regulator of salt stress response and abscisic acid signaling. This study summarizes the structure characterized by molecular formula and protein-binding domains by different bioinformatics tools such as Expasy translate tool, Expasy Portparam, Swiss Prot and Inter Pro Scan, Clustal W tool regulatory procedure of GRASS gene components, also their reactions to both biotic and Abiotic stresses. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=GRAS37" title="GRAS37">GRAS37</a>, <a href="https://publications.waset.org/abstracts/search?q=gene" title=" gene"> gene</a>, <a href="https://publications.waset.org/abstracts/search?q=bioinformatics" title=" bioinformatics"> bioinformatics</a>, <a href="https://publications.waset.org/abstracts/search?q=tool" title=" tool"> tool</a> </p> <a href="https://publications.waset.org/abstracts/185944/characterization-of-gras37-gibberellin-acid-insensitive-gai-repressor-rga-and-scarecrow-scr-gene-by-using-bioinformatics-tools" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/185944.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">53</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">6868</span> Effects of Epinephrine on Gene Expressions during the Metamorphosis of Pacific Oyster Crassostrea gigas</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Fei%20Xu">Fei Xu</a>, <a href="https://publications.waset.org/abstracts/search?q=Guofan%20Zhang"> Guofan Zhang</a>, <a href="https://publications.waset.org/abstracts/search?q=Xiao%20Liu"> Xiao Liu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Many major marine invertebrate phyla are characterized by indirect development. These animals transit from planktonic larvae to benthic adults via settlement and metamorphosis, which has many advantages for organisms to adapt marine environment. Studying the biological process of metamorphosis is thus a key to understand the origin and evolution of indirect development. Although the mechanism of metamorphosis has been largely studied on their relationships with the marine environment, microorganisms, as well as the neurohormones, little is known on the gene regulation network (GRN) during metamorphosis. We treated competent oyster pediveligers with epinephrine, which was known to be able to effectively induce oyster metamorphosis, and analyzed the dynamics of gene and proteins with transcriptomics and proteomics methods. The result indicated significant upregulation of protein synthesis system, as well as some transcription factors including Homeobox, basic helix-loop-helix, and nuclear receptors. The result suggested the GRN complexity of the transition stage during oyster metamorphosis. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=indirect%20development" title="indirect development">indirect development</a>, <a href="https://publications.waset.org/abstracts/search?q=gene%20regulation%20network" title=" gene regulation network"> gene regulation network</a>, <a href="https://publications.waset.org/abstracts/search?q=protein%20synthesis" title=" protein synthesis"> protein synthesis</a>, <a href="https://publications.waset.org/abstracts/search?q=transcription%20factors" title=" transcription factors"> transcription factors</a> </p> <a href="https://publications.waset.org/abstracts/104901/effects-of-epinephrine-on-gene-expressions-during-the-metamorphosis-of-pacific-oyster-crassostrea-gigas" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/104901.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">139</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">6867</span> Characterization of the GntR Family Transcriptional Regulator Rv0792c: A Potential Drug Target for Mycobacterium tuberculosis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Thanusha%20D.%20Abeywickrama">Thanusha D. Abeywickrama</a>, <a href="https://publications.waset.org/abstracts/search?q=Inoka%20C.%20Perera"> Inoka C. Perera</a>, <a href="https://publications.waset.org/abstracts/search?q=Genji%20Kurisu"> Genji Kurisu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Tuberculosis, considered being as the ninth leading cause of death worldwide, cause from a single infectious agent M. tuberculosis and the drug resistance nature of this bacterium is a continuing threat to the world. Therefore TB preventing treatment is expanding, where this study designed to analyze the regulatory mechanism of GntR transcriptional regulator gene Rv0792c, which lie between several genes codes for some hypothetical proteins, a monooxygenase and an oxidoreductase. The gene encoding Rv0792c was cloned into pET28a and expressed protein was purified to near homogeneity by Nickel affinity chromatography. It was previously reported that the protein binds within the intergenic region (BS region) between Rv0792c gene and monooxygenase (Rv0793). This resulted in binding of three protein molecules with the BS region suggesting tight control of monooxygenase as well as its own gene. Since monooxygenase plays a key role in metabolism, this gene may have a global regulatory role. The natural ligand for this regulator is still under investigation. In relation to the Rv0792 protein structure, a Circular Dichroism (CD) spectrum was carried out to determine its secondary structure elements. Percentage-wise, 17.4% Helix, 21.8% Antiparallel, 5.1% Parallel, 12.3% turn and 43.5% other were revealed from CD spectrum data under room temperature. Differential Scanning Calorimetry (DSC) was conducted to assess the thermal stability of Rv0792, which the melting temperature of protein is 57.2 ± 0.6 °C. The graph of heat capacity (Cp) versus temperature for the best fit was obtained for non-two-state model, which concludes the folding of Rv0792 protein occurs through stable intermediates. Peak area (∆HCal ) and Peak shape (∆HVant ) was calculated from the graph and ∆HCal / ∆HVant was close to 0.5, suggesting dimeric nature of the protein. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=CD%20spectrum" title="CD spectrum">CD spectrum</a>, <a href="https://publications.waset.org/abstracts/search?q=DSC%20analysis" title=" DSC analysis"> DSC analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=GntR%20transcriptional%20regulator" title=" GntR transcriptional regulator"> GntR transcriptional regulator</a>, <a href="https://publications.waset.org/abstracts/search?q=protein%20structure" title=" protein structure"> protein structure</a> </p> <a href="https://publications.waset.org/abstracts/88842/characterization-of-the-gntr-family-transcriptional-regulator-rv0792c-a-potential-drug-target-for-mycobacterium-tuberculosis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/88842.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">222</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">6866</span> Intelligent CRISPR Design for Bone Regeneration</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yu-Chen%20Hu">Yu-Chen Hu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Gene editing by CRISPR and gene regulation by microRNA or CRISPR activation have dramatically changed the way to manipulate cellular gene expression and cell fate. In recent years, various gene editing and gene manipulation technologies have been applied to control stem cell differentiation to enhance tissue regeneration. This research will focus on how to develop CRISPR, CRISPR activation (CRISPRa), CRISPR inhibition (CRISPRi), as well as bi-directional CRISPR-AI gene regulation technologies to control cell differentiation and bone regeneration. Moreover, in this study, CRISPR/Cas13d-mediated RNA editng for miRNA editing and bone regeneration will be discussed. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=gene%20therapy" title="gene therapy">gene therapy</a>, <a href="https://publications.waset.org/abstracts/search?q=bone%20regeneration" title=" bone regeneration"> bone regeneration</a>, <a href="https://publications.waset.org/abstracts/search?q=stem%20cell" title=" stem cell"> stem cell</a>, <a href="https://publications.waset.org/abstracts/search?q=CRISPR" title=" CRISPR"> CRISPR</a>, <a href="https://publications.waset.org/abstracts/search?q=gene%20regulation" title=" gene regulation"> gene regulation</a> </p> <a href="https://publications.waset.org/abstracts/168750/intelligent-crispr-design-for-bone-regeneration" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/168750.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">90</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6865</span> Common Regulatory Mechanisms Reveals Links between Aberrant Glycosylation and Biological Hallmarks in Cancer</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jahanshah%20Ashkani">Jahanshah Ashkani</a>, <a href="https://publications.waset.org/abstracts/search?q=Kevin%20J.%20Naidoo"> Kevin J. Naidoo</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Glycosylation is the major posttranslational modification (PTM) process in cellular development. In tumour development, it is marked by structural alteration of carbohydrates (glycans) that is the result of aberrant glycosylation. Altered glycan structures affect cell surface ligand-receptor interactions that interfere with the regulation of cell adhesion, migration, and proliferation. The resulting changes in glycan biosynthesis pathways originate from altered expression of glycosyltransferases and glycosidases. While the alteration in glycosylation patterns is a recognized “hallmark of cancer”, the influential overview of the biology of cancer proposes eight hallmarks with no explicit suggestion to connectivity with glycosylation. Recently, we have discovered a connection between the glycosyltransferase gene expression and cancer type and subtype. Here we present an association between aberrant glycosylation and the biological hallmarks of breast cancer by exploring the common regulatory mechanisms at the genomic scale. The result of this study bridges the glycobiological and biological pathways that are accepted hallmarks of cancer by connecting their common regulatory pathways. This is an impetus for further investigation as target therapies of breast cancer are very likely to be uncovered from this. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=aberrant%20glycosylation" title="aberrant glycosylation">aberrant glycosylation</a>, <a href="https://publications.waset.org/abstracts/search?q=biological%20hallmarks" title=" biological hallmarks"> biological hallmarks</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=regulatory%20mechanism" title=" regulatory mechanism"> regulatory mechanism</a> </p> <a href="https://publications.waset.org/abstracts/53462/common-regulatory-mechanisms-reveals-links-between-aberrant-glycosylation-and-biological-hallmarks-in-cancer" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/53462.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">254</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">6864</span> A Local Tensor Clustering Algorithm to Annotate Uncharacterized Genes with Many Biological Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Paul%20Shize%20Li">Paul Shize Li</a>, <a href="https://publications.waset.org/abstracts/search?q=Frank%20Alber"> Frank Alber</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A fundamental task of clinical genomics is to unravel the functions of genes and their associations with disorders. Although experimental biology has made efforts to discover and elucidate the molecular mechanisms of individual genes in the past decades, still about 40% of human genes have unknown functions, not to mention the diseases they may be related to. For those biologists who are interested in a particular gene with unknown functions, a powerful computational method tailored for inferring the functions and disease relevance of uncharacterized genes is strongly needed. Studies have shown that genes strongly linked to each other in multiple biological networks are more likely to have similar functions. This indicates that the densely connected subgraphs in multiple biological networks are useful in the functional and phenotypic annotation of uncharacterized genes. Therefore, in this work, we have developed an integrative network approach to identify the frequent local clusters, which are defined as those densely connected subgraphs that frequently occur in multiple biological networks and consist of the query gene that has few or no disease or function annotations. This is a local clustering algorithm that models multiple biological networks sharing the same gene set as a three-dimensional matrix, the so-called tensor, and employs the tensor-based optimization method to efficiently find the frequent local clusters. Specifically, massive public gene expression data sets that comprehensively cover dynamic, physiological, and environmental conditions are used to generate hundreds of gene co-expression networks. By integrating these gene co-expression networks, for a given uncharacterized gene that is of biologist’s interest, the proposed method can be applied to identify the frequent local clusters that consist of this uncharacterized gene. Finally, those frequent local clusters are used for function and disease annotation of this uncharacterized gene. This local tensor clustering algorithm outperformed the competing tensor-based algorithm in both module discovery and running time. We also demonstrated the use of the proposed method on real data of hundreds of gene co-expression data and showed that it can comprehensively characterize the query gene. Therefore, this study provides a new tool for annotating the uncharacterized genes and has great potential to assist clinical genomic diagnostics. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=local%20tensor%20clustering" title="local tensor clustering">local tensor clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=query%20gene" title=" query gene"> query gene</a>, <a href="https://publications.waset.org/abstracts/search?q=gene%20co-expression%20network" title=" gene co-expression network"> gene co-expression network</a>, <a href="https://publications.waset.org/abstracts/search?q=gene%20annotation" title=" gene annotation"> gene annotation</a> </p> <a href="https://publications.waset.org/abstracts/155115/a-local-tensor-clustering-algorithm-to-annotate-uncharacterized-genes-with-many-biological-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/155115.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">168</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6863</span> How to Modernise the European Competition Network (ECN)</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Dorota%20Galeza">Dorota Galeza</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper argues that networks, such as the ECN and the American network, are affected by certain small events which are inherent to path dependence and preclude the full evolution towards efficiency. It is advocated that the American network is superior to the ECN in many respects due to its greater flexibility and longer history. This stems in particular from the creation of the American network, which was based on a small number of cases. Such a structure encourages further changes and modifications which are not necessarily radical. The ECN, by contrast, was established by legislative action, which explains its rigid structure and resistance to change. This paper is an attempt to transpose the superiority of the American network on to the ECN. It looks at concepts such as judicial cooperation, harmonisation of procedure, peer review and regulatory impact assessments (RIAs), and dispute resolution procedures. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=antitrust" title="antitrust">antitrust</a>, <a href="https://publications.waset.org/abstracts/search?q=competition" title=" competition"> competition</a>, <a href="https://publications.waset.org/abstracts/search?q=networks" title=" networks"> networks</a>, <a href="https://publications.waset.org/abstracts/search?q=path%20dependence" title=" path dependence"> path dependence</a> </p> <a href="https://publications.waset.org/abstracts/3802/how-to-modernise-the-european-competition-network-ecn" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/3802.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">315</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">6862</span> Identification of Mx Gene Polymorphism in Indragiri Hulu duck by PCR-RFLP</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Restu%20Misrianti">Restu Misrianti</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The amino acid variation of Asn (allele A) at position 631 in Mx gene was specific to positive antiviral to avian viral desease. This research was aimed at identifying polymorphism of Mx gene in duck using molecular technique. Polymerase Chain Reaction-Restriction Fragment Length Polymorphism (PCR-RFLP) technique was used to select the genotype of AA, AG and GG. There were thirteen duck from Indragiri Hulu regency (Riau Province) used in this experiment. DNA amplification results showed that the Mx gene in duck is found in a 73 bp fragment. Mx gene in duck did not show any polymorphism. The frequency of the resistant allele (AA) was 0%, while the frequency of the susceptible allele (GG) was 100%. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=duck" title="duck">duck</a>, <a href="https://publications.waset.org/abstracts/search?q=Mx%20gene" title=" Mx gene"> Mx gene</a>, <a href="https://publications.waset.org/abstracts/search?q=PCR" title=" PCR"> PCR</a>, <a href="https://publications.waset.org/abstracts/search?q=RFLP" title=" RFLP"> RFLP</a> </p> <a href="https://publications.waset.org/abstracts/37764/identification-of-mx-gene-polymorphism-in-indragiri-hulu-duck-by-pcr-rflp" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/37764.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">324</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6861</span> Transcriptomine: The Nuclear Receptor Signaling Transcriptome Database</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Scott%20A.%20Ochsner">Scott A. Ochsner</a>, <a href="https://publications.waset.org/abstracts/search?q=Christopher%20M.%20Watkins"> Christopher M. Watkins</a>, <a href="https://publications.waset.org/abstracts/search?q=Apollo%20McOwiti"> Apollo McOwiti</a>, <a href="https://publications.waset.org/abstracts/search?q=David%20L.%20Steffen%20Lauren%20B.%20Becnel"> David L. Steffen Lauren B. Becnel</a>, <a href="https://publications.waset.org/abstracts/search?q=Neil%20J.%20McKenna"> Neil J. McKenna</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Understanding signaling by nuclear receptors (NRs) requires an appreciation of their cognate ligand- and tissue-specific transcriptomes. While target gene regulation data are abundant in this field, they reside in hundreds of discrete publications in formats refractory to routine query and analysis and, accordingly, their full value to the NR signaling community has not been realized. One of the mandates of the Nuclear Receptor Signaling Atlas (NURSA) is to facilitate access of the community to existing public datasets. Pursuant to this mandate we are developing a freely-accessible community web resource, Transcriptomine, to bring together the sum total of available expression array and RNA-Seq data points generated by the field in a single location. Transcriptomine currently contains over 25,000,000 gene fold change datapoints from over 1200 contrasts relevant to over 100 NRs, ligands and coregulators in over 200 tissues and cell lines. Transcriptomine is designed to accommodate a spectrum of end users ranging from the bench researcher to those with advanced bioinformatic training. Visualization tools allow users to build custom charts to compare and contrast patterns of gene regulation across different tissues and in response to different ligands. Our resource affords an entirely new paradigm for leveraging gene expression data in the NR signaling field, empowering users to query gene fold changes across diverse regulatory molecules, tissues and cell lines, target genes, biological functions and disease associations, and that would otherwise be prohibitive in terms of time and effort. Transcriptomine will be regularly updated with gene lists from future genome-wide expression array and expression-sequencing datasets in the NR signaling field. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=target%20gene%20database" title="target gene database">target gene database</a>, <a href="https://publications.waset.org/abstracts/search?q=informatics" title=" informatics"> informatics</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=transcriptomics" title=" transcriptomics"> transcriptomics</a> </p> <a href="https://publications.waset.org/abstracts/6275/transcriptomine-the-nuclear-receptor-signaling-transcriptome-database" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/6275.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> 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