Literature DB >> 23314125

CePa: an R package for finding significant pathways weighted by multiple network centralities.

Zuguang Gu1, Jin Wang.   

Abstract

SUMMARY: CePa is an R package aiming to find significant pathways through network topology information. The package has several advantages compared with current pathway enrichment tools. First, pathway node instead of single gene is taken as the basic unit when analysing networks to meet the fact that genes must be constructed into complexes to hold normal functions. Second, multiple network centralities are applied simultaneously to measure importance of nodes from different aspects to make a full view on the biological system. CePa extends standard pathway enrichment methods, which include both over-representation analysis procedure and gene-set analysis procedure. CePa has been evaluated with high performance on real-world data, and it can provide more information directly related to current biological problems. AVAILABILITY: CePa is available at the Comprehensive R Archive Network (CRAN): http://cran.r-project.org/web/packages/CePa/

Mesh:

Year:  2013        PMID: 23314125     DOI: 10.1093/bioinformatics/btt008

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  21 in total

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Authors:  Michael A Mooney; Joel T Nigg; Shannon K McWeeney; Beth Wilmot
Journal:  Trends Genet       Date:  2014-08-22       Impact factor: 11.639

2.  Predicting mechanism of action of cellular perturbations with pathway activity signatures.

Authors:  Yan Ren; Siva Sivaganesan; Nicholas A Clark; Lixia Zhang; Jacek Biesiada; Wen Niu; David R Plas; Mario Medvedovic
Journal:  Bioinformatics       Date:  2020-09-15       Impact factor: 6.937

3.  Druggable transcriptomic pathways revealed in Parkinson's patient-derived midbrain neurons.

Authors:  Mark van den Hurk; Shong Lau; Maria C Marchetto; Jerome Mertens; Shani Stern; Olga Corti; Alexis Brice; Beate Winner; Jürgen Winkler; Fred H Gage; Cedric Bardy
Journal:  NPJ Parkinsons Dis       Date:  2022-10-18

4.  Functional network community detection can disaggregate and filter multiple underlying pathways in enrichment analyses.

Authors:  Lia X Harrington; Gregory P Way; Jennifer A Doherty; Casey S Greene
Journal:  Pac Symp Biocomput       Date:  2018

5.  IPI59: An Actionable Biomarker to Improve Treatment Response in Serous Ovarian Carcinoma Patients.

Authors:  J Choi; S Ye; K H Eng; K Korthauer; W H Bradley; J S Rader; C Kendziorski
Journal:  Stat Biosci       Date:  2016-03-29

6.  Pathway enrichment analysis and visualization of omics data using g:Profiler, GSEA, Cytoscape and EnrichmentMap.

Authors:  Jüri Reimand; Ruth Isserlin; Veronique Voisin; Mike Kucera; Christian Tannus-Lopes; Asha Rostamianfar; Lina Wadi; Mona Meyer; Jeff Wong; Changjiang Xu; Daniele Merico; Gary D Bader
Journal:  Nat Protoc       Date:  2019-02       Impact factor: 13.491

7.  Identification of Novel Breast Cancer Subtype-Specific Biomarkers by Integrating Genomics Analysis of DNA Copy Number Aberrations and miRNA-mRNA Dual Expression Profiling.

Authors:  Dongguo Li; Hong Xia; Zhen-ya Li; Lin Hua; Lin Li
Journal:  Biomed Res Int       Date:  2015-04-15       Impact factor: 3.411

8.  LEGO: a novel method for gene set over-representation analysis by incorporating network-based gene weights.

Authors:  Xinran Dong; Yun Hao; Xiao Wang; Weidong Tian
Journal:  Sci Rep       Date:  2016-01-11       Impact factor: 4.379

9.  CPA: a web-based platform for consensus pathway analysis and interactive visualization.

Authors:  Hung Nguyen; Duc Tran; Jonathan M Galazka; Sylvain V Costes; Afshin Beheshti; Juli Petereit; Sorin Draghici; Tin Nguyen
Journal:  Nucleic Acids Res       Date:  2021-07-02       Impact factor: 16.971

10.  R-based software for the integration of pathway data into bioinformatic algorithms.

Authors:  Frank Kramer; Michaela Bayerlová; Tim Beißbarth
Journal:  Biology (Basel)       Date:  2014-02-07
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