A data driven approach reveals disease similarity on a molecular level

Could there be unexpected similarities between different studies, diseases, or treatments, on a molecular level due to common biological mechanisms involved? To answer this question, we develop a method for computing similarities between empirical, statistical distributions of high-dimensional, low-sample datasets, and apply it on hundreds of -omics studies. The similarities lead to dataset-to-dataset networks visualizing the landscape of a large portion of biological data. Potentially interesting similarities connecting studies of different diseases are assembled in a disease-to-disease network. Exploring it, we discover numerous non-trivial connections between Alzheimer’s disease and schizophrenia, asthma and psoriasis, or liver cancer and obesity, to name a few. We then present a method that identifies the molecular quantities and pathways that contribute the most to the identified similarities and could point to novel drug targets or provide biological insights. The proposed method acts as a “statistical telescope” providing a global view of the constellation of biological data; readers can peek through it at: http://datascope.csd.uoc.gr:25000/ . Different phenotypes (e.g., diseases) may be distinct on a macroscopic level, however, they may share several common biological mechanisms on a molecular level. The molecular similarities should be reflected as statistical similarities in corresponding -omics data measurements. A method is presented to identify these statistical similarities. It is then applied to hundreds of public -omics datasets stemming from different studies, creating a network of diseases (phenotypes in general) whose statistical molecular patterns are similar. We discover numerous non-trivial connections on a molecular level between Alzheimer's disease and schizophrenia, asthma and psoriasis, liver cancer and obesity, to name a few. The quantities and pathways that contribute the most to the identified similarities are also reported. They could point to novel drug targets or biological insights.