Reference: Deng X, et al. (2008) A Hidden Markov Model approach to predicting yeast gene function from sequential gene expression data. Int J Bioinform Res Appl 4(3):263-73

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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.

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Journal Article | Research Support, N.I.H., Extramural
Authors
Deng X, Geng H, Ali HH
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