Title: A Hidden Markov Model approach to predicting yeast gene function from sequential gene expression data
Authors: Xutao Deng, Huimin Geng, Hesham H. Ali
Addresses: 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
Abstract: Existing data mining tools can only achieve about 40% precision in function prediction of unannotated genes. We developed a gene function prediction tool based on profile Hidden Markov Models (HMMs). Each function class was modelled using a distinct HMM whose parameters were trained using yeast time-series gene expression profiles. Two structural variants of HMMs were designed and tested, each of them on 40 function classes. The highest overall prediction precision achieved was 67% using double-split HMM with leave-one-out cross-validation. We also attempted to generalise HMMs to dynamic Bayesian networks for gene function prediction using heterogeneous data sets.
Keywords: hidden Markov models; HMM; function prediction; gene expression; bioinformatics; yeast gene function.
International Journal of Bioinformatics Research and Applications, 2008 Vol.4 No.3, pp.263 - 273
Available online: 17 Jul 2008 *Full-text access for editors Access for subscribers Purchase this article Comment on this article