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

ABSTRACT: 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

SUBMITTER: Lee YS 

PROVIDER: S-EPMC7374759 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature