Computational Methods for Detecting Multi-gene Relationships in Gene Expression Data

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Gene coexpression has emerged over the last several years as a powerful bioinformatics tool. Coexpressed genes may be involved in the same biological process, and thus coexpression networks are often used to investigate gene function. Methods for detecting, analyzing and clustering pairs of coexpressed genes are now well developed. Pairwise coexpression, however, is clearly too simplistic to describe the complex relationships between genes, since these relationships may involve multiple genes and can vary depending on the biological context. To address this limitation, we propose to develop computational methods for detecting and analyzing multi-gene relationships in gene expression data, and to use these methods to solve problems in molecular biology. These problems will include the reconstruction of genetic regulatory networks, the detection of modulators of gene regulation, and improved tools for gene function prediction.