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

Xutao Deng, Huimin Geng, Hesham H. Ali

Research output: Contribution to journalArticle

2 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)263-273
Number of pages11
JournalInternational Journal of Bioinformatics Research and Applications
Volume4
Issue number3
DOIs
StatePublished - Jul 2008

Keywords

  • Bioinformatics
  • Function prediction
  • Gene expression
  • HMM
  • Hidden Markov Model

ASJC Scopus subject areas

  • Biomedical Engineering
  • Health Informatics
  • Clinical Biochemistry
  • Health Information Management

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