PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

POIMs: Positional Oligomer Importance Matrices - Understanding Support Vector Machine Based Signal Detectors
Alexander Zien, Sören Sonnenburg, Petra Philips and Gunnar Rätsch
Bioinformatics Volume 24, Number 13, i6, 2008.

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. Availability: All source code, datasets, tables and figures are available at http://www.fml.tuebingen.mpg.de/raetsch/projects/POIM. Contact: Soeren.Sonnenburg@first.fraunhofer.de Supplementary information: Supplementary data are available at Bioinformatics online.

EPrint Type:Article
Additional Information:Web site: http://www.fml.tuebingen.mpg.de/raetsch/suppl/POIM/
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Theory & Algorithms
ID Code:5474
Deposited By:Alexander Zien
Deposited On:10 October 2009