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 the knowledge about the involved samples and genes for selecting genes bearing significant biological relevance. In this work, the authors 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.