Gene functional classification from heterogeneous data
Paul Pavlidis, Jason Weston, Jinsong Cai and William Noble
Proceedings of the Fifth International Conference on Computational
Molecular Biology, April 21-24, 2001. pp. 242-248.
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
Yeast phylogenetic profiles and expression