NDLI: Identifying biologically relevant genes via multiple heterogeneous data sources

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Identifying biologically relevant genes via multiple heterogeneous data sources

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Identifying biologically relevant genes via multiple heterogeneous data sources

Content Provider	ACM Digital Library
Author	Wang, Jiangxin Chang, Yung Zhao, Zheng Liu, Huan Ye, Jieping
Abstract	Selection of genes that are differentially expressed and critical to a particular biological process has been a major challenge in post-array analysis. Recent development in bioinformatics has made various data sources available such as mRNA and miRNA expression profiles, biological pathway and gene annotation, etc. Efficient and effective integration of multiple data sources helps enrich our knowledge about the involved samples and genes for selecting genes bearing significant biological relevance. In this work, we studied a novel problem of multi-source gene selection: given multiple heterogeneous data sources (or data sets), select genes from expression profiles by integrating information from various data sources. We investigated how to effectively employ information contained in multiple data sources to extract an intrinsic global geometric pattern and use it in covariance analysis for gene selection. We designed and conducted experiments to systematically compare the proposed approach with representative methods in terms of statistical and biological significance, and showed the efficacy and potential of the proposed approach with promising findings.
Starting Page	839
Ending Page	847
Page Count	9
File Format	PDF
ISBN	9781605581934
DOI	10.1145/1401890.1401990
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2008-08-24
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Information integration Gene selection Bioinformatics
Content Type	Text
Resource Type	Article

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