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 physico-chemical and sequence based techniques.
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Uren, P., Cameron-Jones, R.M., Sale, A. (2006). Promoter Prediction Using Physico-Chemical Properties of DNA. In: R. Berthold, M., Glen, R.C., Fischer, I. (eds) Computational Life Sciences II. CompLife 2006. Lecture Notes in Computer Science(), vol 4216. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11875741_3
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DOI: https://doi.org/10.1007/11875741_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-45767-1
Online ISBN: 978-3-540-45768-8
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