Understanding and predicting disease relationships through similarity fusion


Motivation: 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. Results: We apply this method to 6 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. Availability: Freely available under the MIT license at https://github.com/e-oerton/disease-similarity-fusion".

Bioinformatics 2018