Sequence alignment kernel for recognition of promoter regions | Zendy

Leo I. Gordon | Zendy; Alexey Chervonenkis | Zendy; Alex Gammerman | Zendy; Ilham A. Shahmuradov | Zendy; Victor Solovyev | Zendy

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Sequence alignment kernel for recognition of promoter regions

Author(s) -

Leo I. Gordon,

Alexey Chervonenkis,

Alex Gammerman,

Ilham A. Shahmuradov,

Victor Solovyev

Publication year - 2003

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btg265

Subject(s) - coding (social sciences) , computer science , word error rate , coding region , kernel (algebra) , support vector machine , pattern recognition (psychology) , transcription (linguistics) , computational biology , genome , kernel method , artificial intelligence , algorithm , gene , mathematics , biology , genetics , statistics , combinatorics , linguistics , philosophy

In this paper we propose a new method for recognition of prokaryotic promoter regions with startpoints of transcription. The method is based on Sequence Alignment Kernel, a function reflecting the quantitative measure of match between two sequences. This kernel function is further used in Dual SVM, which performs the recognition. Several recognition methods have been trained and tested on positive data set, consisting of 669 sigma70-promoter regions with known transcription startpoints of Escherichia coli and two negative data sets of 709 examples each, taken from coding and non-coding regions of the same genome. The results show that our method performs well and achieves 16.5% average error rate on positive & coding negative data and 18.6% average error rate on positive & non-coding negative data.

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