Reference: Deng X, et al. (2008)
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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.
- Reference Type
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Journal Article |
Research Support, N.I.H., Extramural
- Authors
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Deng X,
Geng H,
Ali HH
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Gene Ontology Annotations
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Evidence ID |
Analyze ID |
Gene/Complex |
Systematic Name/Complex Accession |
Qualifier |
Gene Ontology Term ID |
Gene Ontology Term |
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Annotation Extension |
Evidence |
Method |
Source |
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Reference |
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Analyze ID |
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Disease Ontology Term |
Disease Ontology Term ID |
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Reference |
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Analyze ID |
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Regulator Systematic Name |
Target |
Target Systematic Name |
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Happens During |
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Reference |