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Kernel-based data fusion and its application to protein function prediction in yeast.

G R G Lanckriet·M Deng·N Cristianini·M I Jordan·W S Noble
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Abstract

Kernel methods provide a principled framework in which to represent many types of data, including vectors, strings, trees and graphs. As such, these methods are useful for drawing inferences about biological phenomena. We describe a method for combining multiple kernel representations in an optimal fashion, by formulating the problem as a convex optimization problem that can be solved using semidefinite programming techniques. The method is applied to the problem of predicting yeast protein functional classifications using a support vector machine (SVM) trained on five types of data. For this problem, the new method performs better than a previously-described Markov random field method, and better than the SVM trained on any single type of data.

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AlgorithmsArtificial IntelligenceComputational BiologyDatabases, ProteinMarkov ChainsProteomicsSaccharomyces cerevisiaeSaccharomyces cerevisiae ProteinsComparative StudyJournal ArticleResearch Support, Non-U.S. Gov'tResearch Support, U.S. Gov't, Non-P.H.S.

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G R G Lanckriet, M Deng, N Cristianini, M I Jordan, W S Noble. 2004. Kernel-based data fusion and its application to protein function prediction in yeast.. https://doi.org/10.1142/9789812704856_0029

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