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This article is part of the supplement: Neural Information Processing Systems (NIPS) workshop on New Problems and Methods in Computational Biology

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A new pairwise kernel for biological network inference with support vector machines

Jean-Philippe Vert1*, Jian Qiu2 and William S Noble23

Author Affiliations

1 Centre for Computational Biology, Ecole des Mines de Paris, 35 rue Saint-Honoré, 77300 Fontainebleau, France

2 Department of Genome Sciences, University of Washington, 1705 NE Pacific Street, Seattle, WA 98195, USA

3 Department of Computer Science and Engineering, University of Washington, 1705 NE Pacific Street, Seattle, WA 98195, USA

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BMC Bioinformatics 2007, 8(Suppl 10):S8  doi:10.1186/1471-2105-8-S10-S8

Published: 21 December 2007



Much recent work in bioinformatics has focused on the inference of various types of biological networks, representing gene regulation, metabolic processes, protein-protein interactions, etc. A common setting involves inferring network edges in a supervised fashion from a set of high-confidence edges, possibly characterized by multiple, heterogeneous data sets (protein sequence, gene expression, etc.).


Here, we distinguish between two modes of inference in this setting: direct inference based upon similarities between nodes joined by an edge, and indirect inference based upon similarities between one pair of nodes and another pair of nodes. We propose a supervised approach for the direct case by translating it into a distance metric learning problem. A relaxation of the resulting convex optimization problem leads to the support vector machine (SVM) algorithm with a particular kernel for pairs, which we call the metric learning pairwise kernel. This new kernel for pairs can easily be used by most SVM implementations to solve problems of supervised classification and inference of pairwise relationships from heterogeneous data. We demonstrate, using several real biological networks and genomic datasets, that this approach often improves upon the state-of-the-art SVM for indirect inference with another pairwise kernel, and that the combination of both kernels always improves upon each individual kernel.


The metric learning pairwise kernel is a new formulation to infer pairwise relationships with SVM, which provides state-of-the-art results for the inference of several biological networks from heterogeneous genomic data.