Relating Diseases by Integrating Gene Associations and Information Flow through Protein Interaction Network

Identifying similar diseases could potentially provide deeper understanding of their underlying causes, and may even hint at possible treatments. For this purpose, it is necessary to have a similarity measure that reflects the underpinning molecular interactions and biological pathways. We have thus devised a network-based measure that can partially fulfill this goal. Our method assigns weights to all proteins (and consequently their encoding genes) by using information flow from a disease to the protein interaction network and back. Similarity between two diseases is then defined as the cosine of the angle between their corresponding weight vectors. The proposed method also provides a way to suggest disease-pathway associations by using the weights assigned to the genes to perform enrichment analysis for each disease. By calculating pairwise similarities between 2534 diseases, we show that our disease similarity measure is strongly correlated with the probability of finding the diseases in the same disease family and, more importantly, sharing biological pathways. We have also compared our results to those of MimMiner, a text-mining method that assigns pairwise similarity scores to diseases. We find the results of the two methods to be complementary. It is also shown that clustering diseases based on their similarities and performing enrichment analysis for the cluster centers significantly increases the term association rate, suggesting that the cluster centers are better representatives for biological pathways than the diseases themselves. This lends support to the view that our similarity measure is a good indicator of relatedness of biological processes involved i