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

  title={Kernel-Based Data Fusion and Its Application to Protein Function Prediction in Yeast},
  author={Gert R. G. Lanckriet and Minghua Deng and Nello Cristianini and Michael I. Jordan and William Stafford Noble},
  journal={Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing},
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… CONTINUE READING
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