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


BACKGROUND
All source code, datasets, tables and figures are available at http://www.fml.tuebingen.mpg.de/raetsch/projects/POIM.


BACKGROUND
Supplementary data are available at Bioinformatics online.
Sonnenburg
Sören
S
Fraunhofer Institute FIRST, Department IDA, Kekulèstr. 7, 12489 Berlin, Germany. Soeren.Sonnenburg@first.fraunhofer.de
Zien
Alexander
A
Philips
Petra
P
Rätsch
Gunnar
G
D000465:Algorithms; D001185:Artificial Intelligence; D001483:Base Sequence; D004247:DNA / Q000235:genetics*; D008969:Molecular Sequence Data; D010363:Pattern Recognition, Automated / Q000379:methods*; D016415:Sequence Alignment / Q000379:methods*; D017422:Sequence Analysis, DNA / Q000379:methods*
D004247:DNA