Journal
JOURNAL OF COMPUTATIONAL BIOLOGY
Volume 9, Issue 2, Pages 401-411Publisher
MARY ANN LIEBERT INC PUBL
DOI: 10.1089/10665270252935539
Keywords
gene functional classification; phylogenetic profiles; microarray expression analysis
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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. In addition, we describe feature scaling methods for further exploiting prior knowledge of heterogeneity by giving each data type different weights.
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