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Cell Syst. 2019 Feb 27;8(2):152-162.e6. doi: 10.1016/j.cels.2018.12.010. Epub 2019 Jan 23.

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

Author information

1
Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA; Department of Computer Science, Princeton University, Princeton, NJ, USA; School of Biological Sciences, Seoul National University, Seoul, South Korea.
2
Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA; Departments of Computational Mathematics, Science, and Engineering and Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI, USA.
3
Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA.
4
Department of Genetics, Institute of Cancer Research, Oslo University Hospital, Radiumhospitalet, Oslo, Norway; Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway; Department of Clinical Molecular Biology (EpiGen), Division of Medicine, Akershus University Hospital, Lørenskog, Norway.
5
Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA. Electronic address: chandrat@princeton.edu.
6
Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA; Department of Computer Science, Princeton University, Princeton, NJ, USA; Flatiron Institute, Simons Foundation, New York, NY, USA. Electronic address: ogt@cs.princeton.edu.

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, URSAHD (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. URSAHD 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 URSAHD in classifying related nervous system cancers and experimentally verifying novel neuroblastoma-associated genes identified by URSAHD. We highlight the applications for potential targeted drug-repurposing and for quantitatively assessing the molecular response to clinical therapies. URSAHD is freely available for public use, including the use of underlying models, at ursahd.princeton.edu.

KEYWORDS:

drug repurposing; functional genomics; gene expression profiling; human diseases; machine learning; public big data

PMID:
30685436
DOI:
10.1016/j.cels.2018.12.010

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