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Colloquium - Ben-Hur

Eaton South

A Kernel Method for Predicting Protein-Protein Interactions
University of Washington
Asa Ben-Hur photo

Most proteins perform their function by interacting with other proteins. Therefore, information about the network of interactions that occur in a cell can greatly increase our understanding of protein function. We present a kernel method for predicting protein-protein interactions using a combination of data sources, including protein sequences, Gene Ontology annotations, local properties of the network, and interactions in different species. We propose a pairwise kernel that provides a similarity between pairs of proteins, and illustrate its effectiveness in conjunction with a support vector machine classifier. We obtain improved performance by combining several sequence-based kernels based on k-mer frequency, motif and domain composition and by further augmenting the pairwise sequence kernel with features that are based on additional sources of data. In yeast, at a false positive rate of 1%, the classifier retrieves close to 80% of a set of trusted interactions, demonstrating the ability of our method to make accurate predictions despite the sizable fraction of false positives that are known to exist in interaction databases.

The Department holds colloquia throughout the Fall and Spring semesters. These colloquia, open to the public, are typically held on Thursday afternoons, but sometimes occur at other times as well. If you would like to receive email notification of upcoming colloquia, subscribe to our Colloquia Mailing List. If you would like to schedule a colloquium, see Colloquium Scheduling.

Sign language interpreters are available upon request. Please contact Stephanie Morris at least five days prior to the colloquium.

See also:
Department of Computer Science
College of Engineering and Applied Science
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Boulder, CO 80309-0430 USA
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