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Methods Mol Biol. 2019;1939:91-118. doi: 10.1007/978-1-4939-9089-4_6.

Leveraging Big Data to Transform Drug Discovery.

Author information

1
Bakar Computational Health Sciences Institute, University of California, San Francisco, CA, USA.
2
Department of Genetics and Genomic Sciences, Institute of Next Generation Healthcare, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
3
Sema4, A Mount Sinai Venture, Stamford, CT, USA.
4
Bakar Computational Health Sciences Institute, University of California, San Francisco, CA, USA. bin.chen@hc.msu.edu.
5
Department of Pediatrics and Human Development, Michigan State University, Grand Rapids, MI, USA. bin.chen@hc.msu.edu.
6
Department of Pharmacology and Toxicology, Michigan State University, Grand Rapids, MI, USA. bin.chen@hc.msu.edu.

Abstract

The surge of public disease and drug-related data availability has facilitated the application of computational methodologies to transform drug discovery. In the current chapter, we outline and detail the various resources and tools one can leverage in order to perform such analyses. We further describe in depth the in silico workflows of two recent studies that have identified possible novel indications of existing drugs. Lastly, we delve into the caveats and considerations of this process to enable other researchers to perform rigorous computational drug discovery experiments of their own.

KEYWORDS:

Big data; Bioinformatics; Clinical informatics; Drug discovery; Drug repositioning; Drug repurposing; Electronic medical records; Gene expression data; Pharmacogenomics; Systems pharmacology

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