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

Celine Brouard (1) , Marie Szafranski (2, 1) , Florence d'Alché-Buc (3, 1)

1 IBISC - Informatique, Biologie Intégrative et Systèmes Complexes
2 ENSIIE
3 AMIB - Algorithms and Models for Integrative Biology

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.

Keywords: Protein-protein interactions; Link prediction; Kernel methods; Operator-valued kernel; RKHS; semi-supervised learning; transductive learning

Domains: Machine Learning [cs.LG]; Bioinformatics [q-bio.QM]; Quantitative Methods [q-bio.QM]

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⟩