Kernel-based data fusion and its application to protein function prediction in yeast. Export

@article{Lanckriet2004PSB, 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.}, address = {Division of Electrical Engineering, University of California, Berkeley, USA.}, author = {Lanckriet, G. R. and Deng, M. and Cristianini, N. and Jordan, M. I. and Noble, W. S.}, citeulike-article-id = {638220}, citeulike-linkout-0 = {http://view.ncbi.nlm.nih.gov/pubmed/14992512}, citeulike-linkout-1 = {http://www.hubmed.org/display.cgi?uids=14992512}, journal = {Pac Symp Biocomput}, keywords = {function\_prediction, kernel}, pages = {300--311}, posted-at = {2009-03-11 17:31:13}, priority = {2}, title = {Kernel-based data fusion and its application to protein function prediction in yeast.}, url = {http://view.ncbi.nlm.nih.gov/pubmed/14992512}, year = {2004} }

TY - JOUR ID - Lanckriet2004PSB L3 - citeulike-article-id:638220 N2 - 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. JF - Pac Symp Biocomput AD - Division of Electrical Engineering, University of California, Berkeley, USA. EP - 311 TI - Kernel-based data fusion and its application to protein function prediction in yeast. SP - 300 KW - function_prediction KW - kernel AU - Lanckriet, GR AU - Deng, M AU - Cristianini, N AU - Jordan, MI AU - Noble, WS PY - 2004/// UR - http://view.ncbi.nlm.nih.gov/pubmed/14992512 ER -