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SVM Based Prediction of Bacterial Transcription Start Sites

Gordon, James J. & Towsey, Michael W. (2005) SVM Based Prediction of Bacterial Transcription Start Sites. In Proceedings 6th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL’05), July 2005, Brisbane, Australia.

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Abstract

Identifying bacterial promoters is the key to understanding gene expression. Promoters lie in tightly constrained positions relative to the transcription start site (TSS). Knowing the TSS position, one can predict promoter positions to within a few base pairs, and vice versa. As a route to promoter identification, we formally address the problem of TSS prediction, drawing on the RegulonDB database of known (mapped) Escherichia coli TSS locations. The accepted method of finding promoters (and therefore TSSs) is to use position weight matrices (PWMs). We use an alternative approach based on sup-port vector machines (SVMs). In particular, we quantify performance of several SVM models versus a PWM approach, using area under the detection-error tradeoff (DET) curve as a performance metric. SVM models are shown to out-perform the PWM at TSS prediction, and to substantially reduce numbers of false positives, which are the bane of this problem.

Impact and interest:

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1 citations in Web of Science®

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ID Code: 7600
Item Type: Conference Paper
Keywords: Support Vector Machines, Bacterial Promoters
DOI: 10.1007/11508069_58
ISBN: 354026972X
ISSN: 1611-3349
Subjects: Australian and New Zealand Standard Research Classification > MATHEMATICAL SCIENCES (010000) > APPLIED MATHEMATICS (010200) > Biological Mathematics (010202)
Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Pattern Recognition and Data Mining (080109)
Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Artificial Intelligence and Image Processing not elsewhere classified (080199)
Divisions: Past > QUT Faculties & Divisions > Faculty of Science and Technology
Copyright Owner: Copyright 2005 Springer
Copyright Statement: This is the author-version of the work. Conference proceedings published, by Springer Verlag, will be available via SpringerLink. http://www.springer.de/comp/lncs/ Lecture Notes in Computer Science
Deposited On: 11 May 2007
Last Modified: 29 Feb 2012 23:14

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