title: ML2Motif-Reliable extraction of discriminative sequence motifs from learning machines.
abstract: High prediction accuracies are not the only objective to consider when solving problems using machine learning. Instead, particular scientific applications require some explanation of the learned prediction function. For computational biology, positional oligomer importance matrices (POIMs) have been successfully applied to explain the decision of support vector machines (SVMs) using weighted-degree (WD) kernels. To extract relevant biological motifs from POIMs, the motifPOIM method has been devised and showed promising results on real-world data. Our contribution in this paper is twofold: as an extension to POIMs, we propose gPOIM, a general measure of feature importance for arbitrary learning machines and feature sets (including, but not limited to, SVMs and CNNs) and devise a sampling strategy for efficient computation. As a second contribution, we derive a convex formulation of motifPOIMs that leads to more reliable motif extraction from gPOIMs. Empirical evaluations confirm the usefulness of our approach on artificially generated data as well as on real-world datasets.
authors.affiliation: Machine Learning Group, Technical University of Berlin, Berlin, Germany.
authors.forename: Marina M-C
authors.identifier: http://orcid.org/0000-0003-3090-6279
authors.initials: MM
authors.lastname: Vidovic
authors.affiliation: Department of Computer Science, Humboldt University of Berlin, Berlin, Germany.
authors.forename: Marius
authors.initials: M
authors.lastname: Kloft
authors.affiliation: Machine Learning Group, Technical University of Berlin, Berlin, Germany.|Department of Brain and Cognitive Engineering, Korea University, Anam-dong, Seongbuk-gu, Seoul 136-713, Korea.
authors.forename: Klaus-Robert
authors.initials: KR
authors.lastname: Müller
authors.affiliation: Machine Learning Group, Technical University of Berlin, Berlin, Germany.
authors.forename: Nico
authors.initials: N
authors.lastname: Görnitz
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D000069550:Machine Learning; D060388:Support Vector Machine