GENE ONTOLOGY BASED SIMULATION FOR FEATURE SELECTION

Increasing interest among researchers is evidenced for techniques that incorporate prior biological knowledge into gene expression profile classifiers. Specifically, researchers are interested in learning the impact on classification when prior knowledge is incorporated into a classifier rather than just using the statistical properties of the dataset. In this paper, we investigate this impact through simulation. Our simulation relies on an algorithm that generates gene expression data from Gene Ontology. Experiments comparing two classifiers, one trained using only statistical properties and one trained with a combination of statistical properties and Gene Ontology knowledge, are discussed. Experimental results suggest that incorporating Gene Ontology information improves classifier performance. In addition, we discuss the relationship of distance between means of the distributions of the classes and the training sample size on classification accuracy.