Genomic sequencing is no longer a novelty, but gene function annotation remains a key challenge in modern biology. A variety of functional genomics experimental techniques are available, from classic methods such as affinity precipitation to advanced high-throughput techniques such as gene expression microarrays. In the future, more disparate methods will be developed, further increasing the need for integrated computational analysis of data generated by these studies. We address this problem with magic (Multisource Association of Genes by Integration of Clusters), a general framework that uses formal Bayesian reasoning to integrate heterogeneous types of high-throughput biological data (such as large-scale two-hybrid screens and multiple microarray analyses) for accurate gene function prediction. The system formally incorporates expert knowledge about relative accuracies of data sources to combine them within a normative framework. magic provides a belief level with its output that allows the user to vary the stringency of predictions. We applied magic to Saccharomyces cerevisiae genetic and physical interactions, microarray, and transcription factor binding sites data and assessed the biological relevanceof gene groupings using Gene Ontology annotations produced by theSaccaromyces Genome Database. We found that by creating functionalgroupings based on heterogeneous data types, magic improvedaccuracy of the groupings compared with microarray analysis alone. We describeseveral of the biological gene groupings identified.
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