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Characterization of gene expression profiles by interactome‐transcriptome analysis
Author(s) -
Wachi Shinichiro,
Ho Brian,
Wu Reen
Publication year - 2006
Publication title -
the faseb journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.709
H-Index - 277
eISSN - 1530-6860
pISSN - 0892-6638
DOI - 10.1096/fasebj.20.4.a531
Subject(s) - interactome , transcriptome , computational biology , interaction network , context (archaeology) , centrality , microarray analysis techniques , gene , gene regulatory network , biology , biological network , betweenness centrality , systems biology , gene expression profiling , bioinformatics , genetics , gene expression , mathematics , paleontology , combinatorics
A global analysis of protein interaction network, such as the interactome‐transcriptome analysis, provides an effective way to understand the relationships of protein interaction and the biological significance of the differential expression of genes detected by microarray analysis. Previously, we have used such an approach to observe a high centrality of differentially expressed genes associated with lung cancer tissues (Wachi et al, Bioinformatics, Advanced Access, September 27, 2005). Our results support the notion that a topological analysis of cancer genes using protein interaction data will allow the placement of the list of genes, often of the disparate nature, into global, systematic context of the cell. We have sought to determine if the phenomena observed in lung cancer applies to other cancer tissues. Based on these studies, a method will be designed to address a question that will determine whether such high centrality of groups of genes may manifest in a broader set of biological properties. We further extend this analysis to a large number of studies using a publicly available dataset of microarray data, combined with text mining of the experimental annotation to automate the survey of biological conditions where the set of genes differentially expressed are highly centralized in the protein network.