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 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.
Division of Electrical Engineering, University of California, Berkeley, USA.
G R G
GR
Lanckriet
M
M
Deng
N
N
Cristianini
M I
MI
Jordan
W S
WS
Noble
D000465:Algorithms; D001185:Artificial Intelligence; D019295:Computational Biology; D030562:Databases, Protein; D008390:Markov Chains; D040901:Proteomics / Q000706:statistics & numerical data; D012441:Saccharomyces cerevisiae / Q000235:genetics / Q000502:physiology; D029701:Saccharomyces cerevisiae Proteins / Q000235:genetics* / Q000502:physiology*
D029701:Saccharomyces cerevisiae Proteins