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Integrating naive Bayes models and external knowledge to examine copper and iron homeostasis in S. cerevisiae

Department of Cell and Molecular Biology, Life Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720

A novel suite of analytical techniques and visualization tools are applied to 78 published transcription profiling experiments monitoring 5,687 Saccharomyces cerevisiae genes in studies examining cell cycle, responses to stress, and diauxic shift. A naive Bayes model discovered and characterized 45 classes of gene profile vectors. An enrichment measure quantified the association between these classes and specific external knowledge defined by four sets of categories to which genes can be assigned: 106 protein functions, 5 stages of the cell cycle, 265 transcription factors, and 16 chromosomal locations. Many of the 38 genes in class 42 are known to play roles in copper and iron homeostasis. The 17 uncharacterized open reading frames in this class may be involved in similar homeostatic processes; human homologs of two of them could be associated with as yet undefined disease states arising from aberrant metal ion regulation. The Met4, Met31, and Met32 transcription factors may play a role in coregulating genes involved in copper and iron metabolism. Extensions of the simple graphical model used for clustering to learning more complex models of genetic networks are discussed.

molecular profile matrix; gene profile vectors; naive Bayes model; copper and iron metabolism; Bayesian networks

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				M. L. Chow, E. J. Moler, and I. S. Mian Identifying marker genes in transcription profiling data using a mixture of feature relevance experts Physiol Genomics, March 8, 2001; 5(2): 99 - 111. [Abstract] [Full Text] [PDF]