Assigning gene ontology categories (GO) to yeast genes using text-based supervised learning methods

We propose a method for assigning upper level gene ontology terms (GO categories) to genes using relevant documents. This method represents each gene as a vector using relevant documents to the gene. Then, binary classifiers are made for the GO categories using such supervised learning methods as support vector machines and maximum entropy method. We applied this method for assigning GO categories to yeast genes and achieved an average F-measure of 0.67, which is > 0.3 higher than the existing method developed by Raychaudhun et al. We also applied this method to genome-wide annotation for yeast by all GO Slim categories provided by SGD and achieved average F-measures of 0.58, 0.72, and 0.60, respectively, for the three GO parts: cellular component, molecular function, and biological process.