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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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https://hal.archives-ouvertes.fr/hal-00832056
Contributor : Florence d'Alché-Buc <>
Submitted on : Monday, June 10, 2013 - 11:08:01 AM
Last modification on : Tuesday, June 30, 2020 - 11:56:08 AM
Long-term archiving on: : Wednesday, September 11, 2013 - 4:11:05 AM

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Céline 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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