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Pré-Publication, Document De Travail Année : 2006

Metric learning pairwise kernel for graph inference

Jean-Philippe Vert
Jian Qiu
  • Fonction : Auteur
  • PersonId : 836138
William Noble
  • Fonction : Auteur
  • PersonId : 836139

Résumé

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 (MLPK). We demonstrate, using several real biological networks, that this direct approach often improves upon the state-of-the-art SVM for indirect inference with the tensor product pairwise kernel.
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Dates et versions

hal-00108477 , version 1 (20-10-2006)

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Jean-Philippe Vert, Jian Qiu, William Noble. Metric learning pairwise kernel for graph inference. 2006. ⟨hal-00108477⟩
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