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

Céline Brouard 1 Marie Szafranski 2, 1 Florence d'Alché-Buc 3, 1
3 AMIB - Algorithms and Models for Integrative Biology
LIX - Laboratoire d'informatique de l'École polytechnique [Palaiseau], LRI - Laboratoire de Recherche en Informatique, UP11 - Université Paris-Sud - Paris 11, Inria Saclay - Ile de France
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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Céline 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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