A pHMM-ANN based discriminative approach to promoter identification in prokaryote genomic contexts

Mann, Scott, Li, Jinyan and Chen, Yi-Ping Phoebe 2007, A pHMM-ANN based discriminative approach to promoter identification in prokaryote genomic contexts, Nucleic acids research, vol. 35, no. 2, pp. 1-7.

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Title A pHMM-ANN based discriminative approach to promoter identification in prokaryote genomic contexts
Author(s) Mann, Scott
Li, Jinyan
Chen, Yi-Ping Phoebe
Journal name Nucleic acids research
Volume number 35
Issue number 2
Start page 1
End page 7
Publisher Oxford University Press
Place of publication Oxford, England
Publication date 2007
ISSN 0305-1048
1362-4962
Summary The computational approach for identifying promoters on increasingly large genomic sequences has led to many false positives. The biological significance of promoter identification lies in the ability to locate true promoters with and without prior sequence contextual knowledge. Prior approaches to promoter modelling have involved artificial neural networks (ANNs) or hidden Markov models (HMMs), each producing adequate results on small scale identification tasks, i.e. narrow upstream regions. In this work, we present an architecture to support prokaryote promoter identification on large scale genomic sequences, i.e. not limited to narrow upstream regions. The significant contribution involved the hybrid formed via aggregation of the profile HMM with the ANN, via Viterbi scoring optimizations. The benefit obtained using this architecture includes the modelling ability of the profile HMM with the ability of the ANN to associate elements composing the promoter. We present the high effectiveness of the hybrid approach in comparison to profile HMMs and ANNs when used separately. The contribution of Viterbi optimizations is also highlighted for supporting the hybrid architecture in which gains in sensitivity (+0.3), specificity (+0.65) and precision (+0.54) are achieved over existing approaches.
Notes Published online on December 14, 2006
Language eng
Field of Research 080610 Information Systems Organisation
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2006, The Author(s)
Persistent URL http://hdl.handle.net/10536/DRO/DU:30007056

Document type: Journal Article
Collection: School of Engineering and Information Technology
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