A pHMM-ANN based discriminative approach to promoter identification in prokaryote genomic contexts.
A pHMM-ANN based discriminative approach to promoter identification in prokaryote genomic contexts.
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DOI：
10.1093/nar/gkl1024
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发表时间：
2007
影响因子：
14.9
通讯作者：
Chen YP
中科院分区：
文献类型：
--
作者：
Mann S;Li J;Chen YP
在越来越大的基因组序列上识别启动子的计算方法导致了许多假阳性。启动子识别的生物学意义在于能够在有或没有先验序列上下文知识的情况下定位真正的启动子。以前的启动子建模方法包括人工神经网络(ANN)或隐马尔可夫模型(HMM)，每种方法都能在小规模识别任务中产生足够的结果，即狭窄的上游区域。在这项工作中，我们提出了一种支持大规模基因组序列中原核生物启动子识别的体系结构，即不限于狭窄的上游区域。显著的贡献包括通过维特比评分优化将简档HMM与神经网络聚合而形成的混合。使用该体系结构获得的好处包括简档HMM的建模能力和ANN将组成启动子的元素相关联的能力。与单独使用Profile HMM和ANN相比，我们展示了混合方法的高效性。维特比优化在支持混合体系结构方面的贡献也得到了强调，在混合体系结构中，灵敏度(+0.3)、特异度(+0.65)和精度(+0.54)比现有方法获得了提高。
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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影响因子：
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作者：
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通讯作者：
Kulkarni, BD
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