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Positional Oligomer Importance Matrices

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http://pubman.mpdl.mpg.de/cone/persons/resource/persons84960

Sonnenburg,  S
Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons84331

Zien,  A
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons84153

Rätsch,  G
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

Sonnenburg, S., Zien, A., Philips, P., & Rätsch, G. (2007). Positional Oligomer Importance Matrices. Talk presented at NIPS 2007 Workshop on Machine Learning in Computational Biology. Whistler, BC, Canada.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-CAFD-C
Abstract
At the heart of many important bioinformatics problems, such as gene finding and function prediction, is the classification of biological sequences, above all of DNA and proteins. In many cases, the most accurate classifiers are obtained by training SVMs with complex sequence kernels, for instance for transcription starts or splice sites. However, an often criticized downside of SVMs with complex kernels is that it is very hard for humans to understand the learned decision rules and to derive biological insights from them. To close this gap, we introduce the concept of positional oligomer importance matrices (POIMs) and develop an efficient algorithm for their computation. We demonstrate how they overcome the limitations of sequence logos, and how they can be used to find relevant motifs for different biological phenomena in a straight-forward way. Note that the concept of POIMs is not limited to interpreting SVMs, but is applicable to general k8722;mer based scoring systems.