期刊
INTERNATIONAL JOURNAL OF UNCERTAINTY FUZZINESS AND KNOWLEDGE-BASED SYSTEMS
卷 12, 期 -, 页码 63-82出版社
WORLD SCIENTIFIC PUBL CO PTE LTD
DOI: 10.1142/S0218488504002588
关键词
unsupervised learning; Bayesian networks; estimation of distribution algorithms; gene expression data analysis
This paper proposes using estimation of distribution algorithms for unsupervised learning of Bayesian networks, directly as well as within the framework of the Bayesian structural EM algorithm. Both approaches are empirically evaluated in synthetic and real data. Specifically, the evaluation in real data consists in the application of this paper's proposals to gene expression data clustering, i.e., the identification of clusters of genes with similar expression profiles across samples, for the leukemia database. The validation of the clusters of genes that are identified suggests that these may be biologically meaningful.
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