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ML2Motif—Reliable extraction of discriminative sequence motifs from learning machines

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  • Marina M -C Vidovic
  • Marius Kloft
  • Klaus-Robert Müller
  • Nico Görnitz

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.

Suggested Citation

  • Marina M -C Vidovic & Marius Kloft & Klaus-Robert Müller & Nico Görnitz, 2017. "ML2Motif—Reliable extraction of discriminative sequence motifs from learning machines," PLOS ONE, Public Library of Science, vol. 12(3), pages 1-22, March.
    • Handle: RePEc:plo:pone00:0174392
    DOI: 10.1371/journal.pone.0174392
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    1. Asa Ben-Hur & Cheng Soon Ong & Sören Sonnenburg & Bernhard Schölkopf & Gunnar Rätsch, 2008. "Support Vector Machines and Kernels for Computational Biology," PLOS Computational Biology, Public Library of Science, vol. 4(10), pages 1-10, October.
    2. Marina M -C Vidovic & Nico Görnitz & Klaus-Robert Müller & Gunnar Rätsch & Marius Kloft, 2015. "SVM2Motif—Reconstructing Overlapping DNA Sequence Motifs by Mimicking an SVM Predictor," PLOS ONE, Public Library of Science, vol. 10(12), pages 1-23, December.
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