A Hidden Markov Model approach to predicting yeast gene function from sequential gene expression data

Authors:
Xutao Deng;Huimin Geng;Hesham H. Ali
Affiliations:
Cedars-Sinai Medical Center, School of Medicine, University of California, Los Angeles CA 90048, USA.;Department of Pathology and Microbiology, University of Nebraska Medical Center, Omaha NE 68198, USA.;College of Information Science and Technology, University of Nebraska at Omaha, Omaha NE 68182, USA
Venue:
International Journal of Bioinformatics Research and Applications
Year:
2008

Citing 2
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Abstract

Existing data mining tools can only achieve about 40% precisionin function prediction of unannotated genes. We developed a genefunction prediction tool based on profile Hidden Markov Models(HMMs). Each function class was modelled using a distinct HMM whoseparameters were trained using yeast time-series gene expressionprofiles. Two structural variants of HMMs were designed and tested,each of them on 40 function classes. The highest overall predictionprecision achieved was 67% using double-split HMM withleave-one-out cross-validation. We also attempted to generaliseHMMs to dynamic Bayesian networks for gene function predictionusing heterogeneous data sets.