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A White-Box Machine Learning Approach for Revealing Antibiotic Mechanisms of Action

Crossref DOI link: https://doi.org/10.1016/j.cell.2019.04.016

Published Print: 2019-05

Update policy: https://doi.org/10.1016/elsevier_cm_policy

Authors

Yang, Jason H.

Wright, Sarah N.

Hamblin, Meagan

McCloskey, Douglas

Alcantar, Miguel A.

Schrübbers, Lars

Lopatkin, Allison J.

Satish, Sangeeta

Nili, Amir

Palsson, Bernhard O.

Walker, Graham C.

Collins, James J.
Funding

Funding for this research was provided by:

National Science Foundation (1122374)

Novo Nordisk Fonden (R35-ES028303)

Novo Nordisk Fonden (U19-AI111276)

Novo Nordisk Fonden (R01-CA021615)

Defense Threat Reduction Agency (HDTRA1-15-1-0051)

National Institutes of Health (U01-AI124316)

National Institutes of Health (U19-AI111276)

National Institutes of Health (R01-CA021615)

National Institutes of Health (R35-ES028303)

Hansjörg Wyss Institute for Biologically Inspired Engineering, Harvard University

Harvard University

Massachusetts Institute of Technology

Paul G. Allen Frontiers Group

Broad Institute

National Science Foundation

Paul G. Allen Family Foundation
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Text and Data Mining valid from 2019-05-01

Text and Data Mining valid from 2019-05-01

Version of Record valid from 2020-05-30
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This article is maintained by: Elsevier

Article Title: A White-Box Machine Learning Approach for Revealing Antibiotic Mechanisms of Action

Journal Title: Cell

CrossRef DOI link to publisher maintained version: https://doi.org/10.1016/j.cell.2019.04.016

Content Type: article

Copyright: © 2019 Elsevier Inc.