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.
|Title of host publication||BCB 2015 - 6th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics|
|Number of pages||10|
|State||Published - Sep 9 2015|
|Event||6th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics, BCB 2015 - Atlanta, United States|
Duration: Sep 9 2015 → Sep 12 2015
|Name||BCB 2015 - 6th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics|
|Conference||6th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics, BCB 2015|
|Period||9/9/15 → 9/12/15|
Bibliographical noteFunding 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).
Copyright 2015 ACM.
- Biological network
- Gene Ontology
- Gene expression
- Missing gene
ASJC Scopus subject areas
- Health Informatics
- Computer Science Applications
- Biomedical Engineering