A Bayesian network (BN) is a knowledge representation formalism that has proven to be a promising tool for analyzing gene expression data. Several problems still restrict its successful applications. Typical gene expression databases contain measurements for thousands of genes and no more than several hundred samples, but most existing BNs learning algorithms do not scale more than a few hundred variables. Current methods result in poor quality BNs when applied in such high-dimensional datasets. We propose a hybrid constraint-based scored-searching method that is effective for learning gene networks from DNA microarray data. In the first phase of this method, a novel algorithm is used to generate a skeleton BN based on dependency analysis. Then the resulting BN structure is searched by a scoring metric combined with the knowledge learned from the first phase. Computational tests have shown that the proposed method achieves more accurate results than state-of-the-art methods. This method can also be scaled beyond datasets with several hundreds of variables.

• PMID: 17889617
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Journal Article | Research Support, Non-U.S. Gov't

Authors

Wang M, Chen Z, Cloutier S

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