In our attempts to understand cellular function at the molecular level, we must be able to synthesize information from disparate types of genomic data. We consider the problem of inferring gene functional classifications from a heterogeneous data set consisting of DNA microarray expression measurements and phylogenetic profiles from whole-genome sequence comparisons. We demonstrate the application of the support vector machine (SVM) learning algorithm to this functional inference task. Our results suggest the importance of exploiting prior information about the heterogeneity of the data. In particular, we propose an SVM kernel function that is explicitly heterogeneous. We also show how to use knowledge about heterogeneity to aid in feature selection. Gene functional classification from heterogeneous data
Paul Pavlidis, Jason Weston, Jinsong Cai and William Noble Grundy
Proceedings of the Fifth International Conference on Computational Molecular Biology, April 21-24, 2001. pp. 242-248.
Abstract
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Yeast phylogenetic profiles and expression data sets
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