title: A Hidden Markov Model approach to predicting yeast gene function from sequential gene expression data.
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.
authors.affiliation: Cedars-Sinai Medical Center, School of Medicine, University of California, Los Angeles CA 90048, USA. dengx@cshs.org
authors.forename: Xutao
authors.initials: X
authors.lastname: Deng
authors.forename: Huimin
authors.initials: H
authors.lastname: Geng
authors.forename: Hesham H
authors.initials: HH
authors.lastname: Ali
mesh_terms: D003198:Computer Simulation; D020869:Gene Expression Profiling / Q000379:methods*; D015966:Gene Expression Regulation, Fungal / Q000502:physiology*; D008390:Markov Chains; D008954:Models, Biological; D015233:Models, Statistical; D012441:Saccharomyces cerevisiae / Q000378:metabolism*; D029701:Saccharomyces cerevisiae Proteins / Q000378:metabolism*; D015398:Signal Transduction / Q000502:physiology*
chemical_list: D029701:Saccharomyces cerevisiae Proteins