Literature DB >> 23192548

Gene Set/Pathway enrichment analysis.

Jui-Hung Hung1.   

Abstract

Thanks for the dramatic reduction of the costs of high-throughput techniques in modern biotechnology, searching for differentially expressed genes is already a common procedure in identifying biomarkers or signatures of phenotypic states such as diseases or compound treatments. However, in most of the cases, especially in complex diseases, even given a list of biomarkers, the underlying biological mechanisms are still obscure to us. In other words, rather than knowing what genes are involved, we are more interested in discovering the common, collective roles of all these genes. Based on the assumption that genes involved in the same biological processes, functions, or localizations present correlated behaviors in terms of expression levels, signal intensities, allele occurrences, and so on, we can therefore apply statistical tests to find perturbed pathways. Gene Set/Pathway enrichment analysis is one of such techniques; a step-by-step instruction is described in this chapter.

Mesh:

Year:  2013        PMID: 23192548     DOI: 10.1007/978-1-62703-107-3_13

Source DB:  PubMed          Journal:  Methods Mol Biol        ISSN: 1064-3745


  12 in total

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Authors:  Giacomo Baima; Giovanni Iaderosa; Filippo Citterio; Silvia Grossi; Federica Romano; Giovanni N Berta; Nurcan Buduneli; Mario Aimetti
Journal:  Metabolomics       Date:  2021-01-01       Impact factor: 4.290

2.  PathCards: multi-source consolidation of human biological pathways.

Authors:  Frida Belinky; Noam Nativ; Gil Stelzer; Shahar Zimmerman; Tsippi Iny Stein; Marilyn Safran; Doron Lancet
Journal:  Database (Oxford)       Date:  2015-02-27       Impact factor: 3.451

3.  PAGER: constructing PAGs and new PAG-PAG relationships for network biology.

Authors:  Zongliang Yue; Madhura M Kshirsagar; Thanh Nguyen; Chayaporn Suphavilai; Michael T Neylon; Liugen Zhu; Timothy Ratliff; Jake Y Chen
Journal:  Bioinformatics       Date:  2015-06-15       Impact factor: 6.937

4.  Pathway-based gene signatures predicting clinical outcome of lung adenocarcinoma.

Authors:  Ya-Hsuan Chang; Chung-Ming Chen; Hsuan-Yu Chen; Pan-Chyr Yang
Journal:  Sci Rep       Date:  2015-06-04       Impact factor: 4.379

5.  Systems view of adipogenesis via novel omics-driven and tissue-specific activity scoring of network functional modules.

Authors:  Isar Nassiri; Rosario Lombardo; Mario Lauria; Melissa J Morine; Petros Moyseos; Vijayalakshmi Varma; Greg T Nolen; Bridgett Knox; Daniel Sloper; Jim Kaput; Corrado Priami
Journal:  Sci Rep       Date:  2016-07-07       Impact factor: 4.379

6.  Elucidating the modes of action for bioactive compounds in a cell-specific manner by large-scale chemically-induced transcriptomics.

Authors:  Michio Iwata; Ryusuke Sawada; Hiroaki Iwata; Masaaki Kotera; Yoshihiro Yamanishi
Journal:  Sci Rep       Date:  2017-01-10       Impact factor: 4.379

7.  Identification of a robust subpathway-based signature for acute myeloid leukemia prognosis using an miRNA integrated strategy.

Authors:  Huijuan Chang; Qiuying Gao; Wei Ding; Xueqin Qing
Journal:  PLoS One       Date:  2018-03-23       Impact factor: 3.240

8.  PSFC: a Pathway Signal Flow Calculator App for Cytoscape.

Authors:  Lilit Nersisyan; Graham Johnson; Megan Riel-Mehan; Alexander Pico; Arsen Arakelyan
Journal:  F1000Res       Date:  2015-08-05

9.  Network-Based Methods for Identifying Key Active Proteins in the Extracellular Electron Transfer Process in Shewanella oneidensis MR-1.

Authors:  Dewu Ding; Xiao Sun
Journal:  Genes (Basel)       Date:  2018-01-16       Impact factor: 4.096

10.  The Ability of Different Imputation Methods to Preserve the Significant Genes and Pathways in Cancer.

Authors:  Rosa Aghdam; Taban Baghfalaki; Pegah Khosravi; Elnaz Saberi Ansari
Journal:  Genomics Proteomics Bioinformatics       Date:  2017-12-13       Impact factor: 7.691

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