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Hassani-Pak, Keywan

Journal of Integrative Bioinformatics

Editor-in-Chief: Schreiber, Falk / Hofestädt, Ralf

Managing Editor: Sommer, Björn

Ed. by Baumbach, Jan / Chen, Ming / Orlov, Yuriy / Allmer, Jens

Editorial Board: Giorgetti, Alejandro / Harrison, Andrew / Kochetov, Aleksey / Krüger, Jens / Ma, Qi / Matsuno, Hiroshi / Mitra, Chanchal K. / Pauling, Josch K. / Rawlings, Chris / Fdez-Riverola, Florentino / Romano, Paolo / Röttger, Richard / Shoshi, Alban / Soares, Siomar de Castro / Taubert, Jan / Tauch, Andreas / Yousef, Malik / Weise, Stephan

4 Issues per year


CiteScore 2017: 0.77

SCImago Journal Rank (SJR) 2017: 0.336

Open Access
Online
ISSN
1613-4516
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Volume 9, Issue 2

Issues

Network Expansion and Pathway Enrichment Analysis towards Biologically Significant Findings from Microarrays

Xiaogang Wu
  • School of Informatics, Indiana University, Indianapolis, IN 46202, USA United States of America
  • MedeoLinx, LLC, Indianapolis, IN 46280, United States of America
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Christoph Reinhard / Shuyu D. Li / Hui Huang / Tao Wei / Ragini Pandey / Jake Y. Chen
  • Corresponding author
  • School of Informatics, Indiana University, Indianapolis, IN 46202, USA United States of America
  • MedeoLinx, LLC, Indianapolis, IN 46280, United States of America
  • Email
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
Published Online: 2016-10-18 | DOI: https://doi.org/10.1515/jib-2012-213

Summary

In many cases, crucial genes show relatively slight changes between groups of samples (e.g. normal vs. disease), and many genes selected from microarray differential analysis by measuring the expression level statistically are also poorly annotated and lack of biological significance. In this paper, we present an innovative approach - network expansion and pathway enrichment analysis (NEPEA) for integrative microarray analysis. We assume that organized knowledge will help microarray data analysis in significant ways, and the organized knowledge could be represented as molecular interaction networks or biological pathways. Based on this hypothesis, we develop the NEPEA framework based on network expansion from the human annotated and predicted protein interaction (HAPPI) database, and pathway enrichment from the human pathway database (HPD). We use a recently-published microarray dataset (GSE24215) related to insulin resistance and type 2 diabetes (T2D) as case study, since this study provided a thorough experimental validation for both genes and pathways identified computationally from classical microarray analysis and pathway analysis. We perform our NEPEA analysis for this dataset based on the results from the classical microarray analysis to identify biologically significant genes and pathways. Our findings are not only consistent with the original findings mostly, but also obtained more supports from other literatures.

About the article

Published Online: 2016-10-18

Published in Print: 2012-06-01


Citation Information: Journal of Integrative Bioinformatics, Volume 9, Issue 2, Pages 113–125, ISSN (Online) 1613-4516, DOI: https://doi.org/10.1515/jib-2012-213.

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© 2012 The Author(s). Published by Journal of Integrative Bioinformatics.. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. BY-NC-ND 4.0

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