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Conference papers
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.
Cited literature [14 references]
https://hal.archives-ouvertes.fr/hal-00830428
Contributor : Céline Brouard <>
Submitted on : Wednesday, June 5, 2013 - 9:34:35 AM
Last modification on : Monday, January 25, 2021 - 8:28:02 PM
Long-term archiving on: : Friday, September 6, 2013 - 4:10:10 AM
Contributor : Céline Brouard <>
Submitted on : Wednesday, June 5, 2013 - 9:34:35 AM
Last modification on : Monday, January 25, 2021 - 8:28:02 PM
Long-term archiving on: : Friday, September 6, 2013 - 4:10:10 AM
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jobim_2012.pdf
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