Mining plausible patterns from genomic data

The discovery of biologically interpretable knowledge from gene expression data is one of the largest contemporary genomic challenges. As large volumes of expression data are being generated, there is a great need for automated tools that provide the means to analyze them. However, the same tools can provide an overwhelming number of candidate hypotheses which can hardly be manually exploited by an expert. An additional knowledge helping to focus automatically on the most plausible candidates only can up-value the experiment significantly. Background knowledge available in literature databases, biological ontologies and other sources can be used for this purpose. In this paper we propose and verify a methodology that enables to effectively mine and represent meaningful over-expression patterns. Each pattern represents a bi-set of a gene group over-expressed in a set of biological situations. The originality of the framework consists in its constraint-based nature and an effective cross-fertilization of constraints based on expression data and background knowledge. The result is a limited set of candidate patterns that are most likely interpretable by biologists. Supplemental automatic interpretations serve to ease this process. Various constraints can generate plausible pattern sets of different characteristics.