Kernel-Based Data Fusion and Its Application to Protein Function Prediction in Yeast

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 semideflnite programming techniques. The method is applied to the problem of predicting yeast protein functional classiflcations using a support vector machine SVM trained on flve types of data. For this problem, the new method performs better than a previously-described Markov random fleld method, and better than the SVM trained on any single type of data.