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Conference Papers Year : 2012

Protein-protein interaction network inference with semi-supervised Output Kernel Regression

Abstract

In this work, we address the problem of protein-protein interaction network inference as a semi-supervised output kernel learning problem. Using the kernel trick in the output space allows one to reduce the problem of learning from pairs to learning a single variable function with values in a Hilbert space. We turn to the Reproducing Kernel Hilbert Space theory devoted to vector- valued functions, which provides us with a general framework for output kernel regression. In this framework, we propose a novel method which allows to extend Output Kernel Regression to semi-supervised learning. We study the relevance of this approach on transductive link prediction using artificial data and a protein-protein interaction network of S. Cerevisiae using a very low percentage of labeled data.
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Dates and versions

hal-00830428 , version 1 (05-06-2013)

Identifiers

  • HAL Id : hal-00830428 , version 1

Cite

Celine Brouard, Marie Szafranski, Florence d'Alché-Buc. Protein-protein interaction network inference with semi-supervised Output Kernel Regression. JOBIM, Jul 2012, Rennes, France. pp.133-136. ⟨hal-00830428⟩
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