Machine learning methods for transcription data integration
IBM Journal of Research and Development - Systems biology
EvoBIO '09 Proceedings of the 7th European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics
An information theoretic approach to assessing gene-ontology-driven similarity and its application
International Journal of Data Mining and Bioinformatics
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Motivation: Several pattern discovery methods have been proposed to detect over-represented motifs in upstream sequences of co-regulated genes, and are for example used to predict cis-acting elements from clusters of co-expressed genes. The clusters to be analyzed are often noisy, containing a mixture of co-regulated and non-co-regulated genes. We propose a method to discriminate co-regulated from non-co-regulated genes on the basis of counts of pattern occurrences in their non-coding sequences. Methods: String-based pattern discovery is combined with discriminant analysis to classify genes on the basis of putative regulatory motifs. Results: The approach is evaluated by comparing the significance of patterns detected in annotated regulons (positive control), random gene selections (negative control) and high-throughput regulons (noisy data) from the yeast Saccharomyces cerevisiae. The classification is evaluated on the annotated regulons, and the robustness and rejection power is assessed with mixtures of co-regulated and random genes. Supplementary information: http://rsat.ulb.ac.be/rsat/