document: Identifying biologically relevant genes via multiple heterogeneous data sources 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 .
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