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  Positional Oligomer Importance Matrices

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 (MLCB 2007). Whistler, BC, Canada. 2007-12-07 - 2007-12-08.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-CAFD-C Version Permalink: http://hdl.handle.net/21.11116/0000-0004-4479-8
Genre: Talk

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 Creators:
Sonnenburg, S1, 2, Author              
Zien, A1, 2, Author              
Philips, P, Author
Rätsch, G3, Author              
Affiliations:
1Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497794              
2Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
3Friedrich Miescher Laboratory, Max Planck Society, Max-Planck-Ring 9, 72076 Tübingen, DE, ou_2575692              

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 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.

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 Dates: 2007-12
 Publication Status: Published online
 Pages: -
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 Rev. Method: -
 Identifiers: BibTex Citekey: 5033
 Degree: -

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Title: NIPS 2007 Workshop on Machine Learning in Computational Biology (MLCB 2007)
Place of Event: Whistler, BC, Canada
Start-/End Date: 2007-12-07 - 2007-12-08
Invited: Yes

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