Gene functional classification by semi-supervised learning from heterogeneous data

Abstract

Gene function discovery is an important and interesting problem in computational analysis of microarray data. In this paper, we investigate the use of a semi-supervised learning algorithm for inferring gene functional classifications from heterogeneous data set consisting of DNA microarray expression measurements and phylogenetic profiles from whole-genome sequence compassions. The semi-supervised learning approach aims at minimizing the disagreement between individual models built from each separate information source by employing a co-updating method and making use of both labeled and unlabeled data. Our results suggest that the semi-supervised approach could be used for gene functional classification. The data sets and the program code used for the experiments can be accessed from our webpage.