Learning interpretable SVMs for biological sequence classification.
BACKGROUND
Support Vector Machines (SVMs)--using a variety of string kernels--have been successfully applied to biological sequence classification problems. While SVMs achieve high classification accuracy they lack interpretability. In many applications, it does not suffice that an algorithm just detects a biological signal in the sequence, but it should also provide means to interpret its solution in order to gain biological insight.


RESULTS
We propose novel and efficient algorithms for solving the so-called Support Vector Multiple Kernel Learning problem. The developed techniques can be used to understand the obtained support vector decision function in order to extract biologically relevant knowledge about the sequence analysis problem at hand. We apply the proposed methods to the task of acceptor splice site prediction and to the problem of recognizing alternatively spliced exons. Our algorithms compute sparse weightings of substring locations, highlighting which parts of the sequence are important for discrimination.


CONCLUSIONS
The proposed method is able to deal with thousands of examples while combining hundreds of kernels within reasonable time, and reliably identifies a few statistically significant positions.
Rätsch
Gunnar
G
Friedrich Miescher Laboratory, Max Planck Society, Spemannstr. 39, Tübingen, Germany. gunnar.raetsch@tuebingen.mpg.de
Sonnenburg
Sören
S
Schäfer
Christin
C
D000465:Algorithms; D020816:Amino Acid Motifs; D001185:Artificial Intelligence; D001665:Binding Sites; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D015233:Models, Statistical; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated; D016415:Sequence Alignment; D020539:Sequence Analysis, Protein; D012984:Software