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

Scott Mann | Zendy; Jinyan Li | Zendy; YiPing Phoebe Chen | Zendy

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

Author(s) -

Scott Mann,

Jinyan Li,

YiPing Phoebe Chen

Publication year - 2006

Publication title -

nucleic acids research

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 9.008

H-Index - 537

eISSN - 1362-4954

pISSN - 0305-1048

DOI - 10.1093/nar/gkl1024

Subject(s) - hidden markov model , biology , viterbi algorithm , identification (biology) , discriminative model , artificial intelligence , computational biology , promoter , artificial neural network , prokaryote , pattern recognition (psychology) , computer science , genetics , machine learning , gene , gene expression , botany

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.

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