A Semi-supervised Learning Approach to Disease Gene Prediction

Discovering human disease-causing genes (disease genes in short) is one of the most challenging problems in bioinformatics and biomedicine, as most diseases are related in some way to our genes. Various methods have been proposed to exploit existing data sources for solving the problem. We aim to develop a novel method to predict disease genes that takes into account the imbalance between known disease genes and unknown disease genes. To this end, our method makes the best of semi-supervised learning, integrating data of human protein-protein interactions and various biological data extracted from multiple proteomic/genomic databases. Experimental evaluation shows high performance of our proposed method. Also, a considerable number of potential disease genes were discovered.