Genome wide functional annotation using mathematical programming

Abstract

Publisher Summary Recent advances in complementary DNA (cDNA) microarray technology have resulted in a surge of gene expression data. Whole-genome hybridization results have been reported for many organisms (yeast and worm) under different experimental conditions. Studies have been reported on various methods for assigning functionality to previously unknown genes. Some of these methods are clustering techniques, self-organizing maps, and knowledge-based support vector machines (SVMs). These techniques use a similarity measure to associate genes with unknown functionality with genes of known functionality. This chapter discusses the genome-wide functional annotation, for yeast data set, based on an SVM strategy. In SVM model, each gene can potentially be assigned to more than one class. A gene is referred to as a positive sample of a given class if it belongs to that class; otherwise the gene is referred to as a negative sample. The viability of the SVM model using yeast data with several missing experimental measurements is established. The SVM model correctly annotated the positive samples in all classes. The negative genes were not so well annotated. This is attributed to the missing data for the negative gene set.