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  POIMs: positional oligomer importance matrices: understanding support vector machine-based signal detectors

Sonnenburg, S., Zien, A., Philips, P., & Rätsch, G. (2008). POIMs: positional oligomer importance matrices: understanding support vector machine-based signal detectors. Bioinformatics, 24(13), i6-i14.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0003-3034-C Version Permalink: http://hdl.handle.net/21.11116/0000-0003-90E0-C
Genre: Conference Paper

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 Creators:
Sonnenburg, S, Author              
Zien, A1, 2, 3, Author              
Philips , P3, 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, ou_2575692              

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 Abstract: Motivation: At the heart of many important bioinformatics problems, such as gene finding and function prediction, is the classification of biological sequences. Frequently the most accurate classifiers are obtained by training support vector machines (SVMs) with complex sequence kernels. However, a cumbersome shortcoming of SVMs is that their learned decision rules are very hard to understand for humans and cannot easily be related to biological facts. Results: To make SVM-based sequence classifiers more accessible and profitable, we introduce the concept of positional oligomer importance matrices (POIMs) and propose an efficient algorithm for their computation. In contrast to the raw SVM feature weighting, POIMs take the underlying correlation structure of k-mer features induced by overlaps of related k-mers into account. POIMs can be seen as a powerful generalization of sequence logos: they allow to capture and visualize sequence patterns that are relevant for the investigated biological phenomena.

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 Dates: 2008-07
 Publication Status: Published in print
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 Identifiers: DOI: 10.1093/bioinformatics/btn170
 Degree: -

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Title: 16th Annual International Conference Intelligent Systems for Molecular Biology (ISMB 2008)
Place of Event: Toronto, Canada
Start-/End Date: 2008-07-19 - 2008-07-23

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Title: Bioinformatics
Source Genre: Journal
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Publ. Info: Oxford : Oxford University Press
Pages: - Volume / Issue: 24 (13) Sequence Number: - Start / End Page: i6 - i14 Identifier: ISSN: 1367-4803
CoNE: https://pure.mpg.de/cone/journals/resource/954926969991