Understanding and predicting disease relationships through similarity fusion.

Abstract

Combining disease relationships across multiple biological levels could aid our understanding of common processes taking place in disease, potentially indicating opportunities for drug sharing. Here, we propose a similarity fusion approach which accounts for differences in information content between different data types, allowing combination of each data type in a balanced manner. We apply this method to six different types of biological data (ontological, phenotypic, literature co-occurrence, genetic association, gene expression and drug indication data) for 84 diseases to create a 'disease map': a network of diseases connected at one or more biological levels. As well as reconstructing known disease relationships, 15% of links in the disease map are novel links spanning traditional ontological classes, such as between psoriasis and inflammatory bowel disease. 62% of links in the disease map represent drug-sharing relationships, illustrating the relevance of the similarity fusion approach to the identification of potential therapeutic relationships. Freely available under the MIT license at https://github.com/e-oerton/disease-similarity-fusion. Supplementary data are available at Bioinformatics online.