Prediction of Drosophila melanogaster gene function using Support Vector Machines

Nicholas Mitsakakis, Zak Razak, Michael Escobar, J Timothy Westwood

While the genomes of hundreds of organisms have been sequenced and good approaches exist for finding protein encoding genes, an important remaining challenge is predicting the functions of the large fraction of genes for which there is no annotation. Large gene expression datasets from microarray experiments already exist and many of these can be used to help assign potential functions to these genes. We have applied Support Vector Machines (SVM), a sigmoid fitting function and a stratified cross-validation approach to analyze a large microarray experiment dataset from Drosophila melanogaster in order to predict possible functions for previously un-annotated genes. A total of approximately 5043 different genes, or about one-third of the predicted genes in the D. melanogaster genome, are represented in the dataset and 1854 (or 37%) of these genes are un-annotated.