Kernel-based data fusion and its application to protein function prediction in yeast
- PMID: 14992512
- DOI: 10.1142/9789812704856_0029
Kernel-based data fusion and its application to protein function prediction in yeast
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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