POIMs: positional oligomer importance matrices—understanding support vector machine-based signal detectors
By Sören Sonnenburg, Alexander Zien, Petra Philips, Gunnar Rätsch. 2008
73 citations recorded by the source corpus.
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.
Subfields: Molecular Biology
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In Molecular Biology
- K. Müller
- Marius Kloft
- Nico Görnitz
- Marina M.-C. Vidovic
- Gunnar Rätsch
- Nicole Krämer
- Fosca Giannotti
- Dino Pedreschi
Citations in the corpus
- A Survey of Methods for Explaining Black Box Models (2018, methodology): “The problem faced in Reference [111] is specific for the classification of DNA sequences and k-mers.”
- The Feature Importance Ranking Measure (2009, methodology): “Here we follow the related but more “intelligent” idea of [17]: to assess the importance of a feature by estimating its total impact on the score of a trained predictor.”
- SVM2Motif—Reconstructing Overlapping DNA Sequence Motifs by Mimicking an SVM Predictor (2015, methodology): “” [15], page 8), we proposed a very efficient numerical framework.”
- Feature Importance Measure for Non-linear Learning Algorithms (2016, background): “In computational biology, positional oligomer importance matrices (POIMs) [7] address the need for interpretation of sophisticated learning machines.”