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Abstract

The ability to locate promoters within a section of DNA is known to be a very difficult and very important task in DNA analysis. We document an approach that incorporates the concept of DNA as a complex molecule using several models of its physico-chemical properties. A support vector machine is trained to recognise promoters by their distinctive physical and chemical properties. We demonstrate that by combining models, we can improve upon the classification accuracy obtained with a single model. We also show that by examining how the predictive accuracy of these properties varies over the promoter, we can reduce the number of attributes needed. Finally, we apply this method to a real-world problem. The results demonstrate that such an approach has significant merit in its own right. Furthermore, they suggest better results from a planned combined approach to promoter prediction using both physicochemical and sequence based techniques.

Item Type:	Book Section
Authors/Creators:	Uren, PJ and Cameron-Jones, RM and Sale, AHJ
Keywords:	promoter prediction, support vector machine, SVM, physicochemical, classifier, DNA, transcription, physico-chemical
Publisher:	Springer-Verlag: Lecture Notes in Computer Science
DOI / ID Number:	10.1007/11875741
Additional Information:	The original publication is available at www.springerlink.com
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