On the comparison of state-and transition-based analysis of biological relevance in gene co-expression networks

Kathryn Cooper, Prasuna Vemuri, Hesham Ali

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Traditional correlation network analysis typically involves creating a network using gene expression data and then identifying biologically relevant clusters from that network by enrichment with Gene Ontology or pathway information. When one wants to examine these networks in a dynamic way-such as between controls versus treatment or over time-a "snapshot" approach is taken by comparing network structures at each time point. The biological relevance of these structures are then reported and compared. In this research, we examine the same "snapshot" networks but focus on the enrichment of changes in structure to determine if these results give any more insight into the mechanisms behind observed phenotypes. Our main hypothesis is that more information, particularly related to potential dynamic changes, can be obtained through transition-based analysis of biological networks. To test this hypothesis, we compare gene expression data from the mouse hippocampus at three different time points: young, middle-aged, and aged, and compare the traditional state-based approach to the dynamic transition-based enrichment approach. In this study we use a clustering approach (SPICi) designed specifically for clustering of large biological networks. The results of this study verify an inconsistency between traditional and dynamic structure identification approaches through biological enrichment. These results highlight an intriguing issue for those performing, critiquing, and using network based approaches in their research-that a black box or workflow type of approach typically used in network based research can be Supplemented with a transitionbased approach to support movement from in silico to in vivo experimentation of target genes.

Original languageEnglish (US)
Title of host publicationProceedings - 2015 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015
Editorslng. Matthieu Schapranow, Jiayu Zhou, Xiaohua Tony Hu, Bin Ma, Sanguthevar Rajasekaran, Satoru Miyano, Illhoi Yoo, Brian Pierce, Amarda Shehu, Vijay K. Gombar, Brian Chen, Vinay Pai, Jun Huan
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1197-1203
Number of pages7
ISBN (Electronic)9781467367981
DOIs
StatePublished - Dec 16 2015
EventIEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015 - Washington, United States
Duration: Nov 9 2015Nov 12 2015

Publication series

NameProceedings - 2015 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015

Other

OtherIEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015
CountryUnited States
CityWashington
Period11/9/1511/12/15

Keywords

  • gene expression
  • network analysis
  • state-based
  • transition-based

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence
  • Health Informatics
  • Biomedical Engineering

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