A Computational Framework for Genome-wide Characterization of the Human Disease Landscape

A key challenge for the diagnosis and treatment of complex human diseases is identifying their molecular basis. Here, we developed a unified computational framework, URSA ^HD (Unveiling RNA Sample Annotation for Human Diseases), that leverages machine learning and the hierarchy of anatomical relationships present among diseases to integrate thousands of clinical gene expression profiles and identify molecular characteristics specific to each of the hundreds of complex diseases. URSA ^HD can distinguish between closely related diseases more accurately than literature-validated genes or traditional differential-expression-based computational approaches and is applicable to any disease, including rare and understudied ones. We demonstrate the utility of URSA ^HD in classifying related nervous system cancers and experimentally verifying novel neuroblastoma-associated genes identified by URSA ^HD . We highlight the applications for potential targeted drug-repurposing and for quantitatively assessing the molecular response to clinical therapies. URSA ^HD is freely available for public use, including the use of underlying models, at ursahd.princeton.edu. Discovering unique properties among diseases is needed to develop targeted treatments, especially for related disorders. To address this, we developed a unified framework, URSA ^HD , which leverages physiological relationships between diseases and integrates thousands of clinical samples across >300 diseases to identify distinct characteristics that can be used to guide biomedical research. We demonstrate applications of URSA ^HD , including guiding hypothesis generation and experiments, drug repurposing, and quantitatively tracking drug response.