Improving biological significance of gene expression biclusters with key missing genes

Shufan Ji, Xing Tian, Jin Chen

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Identifying condition-specific co-expressed gene groups is critical for gene functional and regulatory analysis. However, given that genes with critical functions (such as transcription factors) may not co-express with their target genes, it is insufficient to uncover gene functional associations only from gene expression data. In this paper, we propose a novel integrative biclustering approach to build high quality biclusters from gene expression data, and to identify critical missing genes in biclusters based on Gene Ontology as well. Our approach delivers a complete inter-and intra-bicluster functional relationship, thus provides biologists a clear picture for gene functional association study. We experimented with the Yeast cell cycle and Arabidopsis cold-response gene expression datasets. Experimental results show that a clear inter-and intra-bicluster relationship is identified, and the biological significance of the biclusters is considerably improved.

Original languageEnglish
Title of host publicationBCB 2015 - 6th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics
Pages268-277
Number of pages10
ISBN (Electronic)9781450338530
DOIs
StatePublished - Sep 9 2015
Event6th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics, BCB 2015 - Atlanta, United States
Duration: Sep 9 2015Sep 12 2015

Publication series

NameBCB 2015 - 6th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics

Conference

Conference6th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics, BCB 2015
Country/TerritoryUnited States
CityAtlanta
Period9/9/159/12/15

Bibliographical note

Funding Information:
This research was supported by Chemical Sciences, Geosciences and Biosciences Division, Office of Basic Energy Sciences, Office of Science, U.S. Department of Energy (award number DE-FG02-91ER20021).

Publisher Copyright:
Copyright 2015 ACM.

Keywords

  • Bi-clustering
  • Biological network
  • Gene Ontology
  • Gene expression
  • Missing gene

ASJC Scopus subject areas

  • Software
  • Health Informatics
  • Computer Science Applications
  • Biomedical Engineering

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