A data driven approach reveals disease similarity on a molecular level.

ABSTRACT: 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

SUBMITTER: Lakiotaki K 

PROVIDER: S-EPMC6814739 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature