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A new theoretical angle to semi-supervised output kernel regression for protein-protein interaction network inference

Abstract

Protein-protein interaction network inference is addressed as an output kernel learning task through semi-supervised Output Kernel Regression. Working in the framework of RKHS theory for vector-valued functions, we establish a new representer theorem devoted to semi-supervised least square regression. We then apply it to get a new model and show its relevance using numerical experiments on artificial networks and a protein-protein interaction network dataset using a very low percentage of labeled proteins in a transductive setting.
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Dates and versions

hal-00832056 , version 1 (10-06-2013)

Identifiers

  • HAL Id : hal-00832056 , version 1

Cite

Celine Brouard, Florence d'Alché-Buc, Marie Szafranski. A new theoretical angle to semi-supervised output kernel regression for protein-protein interaction network inference. International Workshop on Machine Learning in Systems Biology, Jul 2011, Vienne, Austria. ⟨hal-00832056⟩
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