Systematic interrogation of diverse Omic data reveals interpretable, robust, and generalizable transcriptomic features of clinically successful therapeutic targets

Andrew D. Rouillard; Mark R. Hurle; Pankaj Agarwal

doi:10.1371/journal.pcbi.1006142

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Open Access

Peer-reviewed

Research Article

Systematic interrogation of diverse Omic data reveals interpretable, robust, and generalizable transcriptomic features of clinically successful therapeutic targets

Andrew D. Rouillard,

Roles Data curation, Formal analysis, Methodology, Software, Visualization, Writing – original draft, Writing – review & editing

Affiliation Computational Biology, GSK, Collegeville, PA, United States of America

http://orcid.org/0000-0002-3502-7161

⨯
Mark R. Hurle,

Roles Conceptualization, Data curation, Methodology, Writing – review & editing

Affiliation Computational Biology, GSK, Collegeville, PA, United States of America
⨯
Pankaj Agarwal

Roles Conceptualization, Methodology, Supervision, Writing – review & editing

* E-mail: pankaj.agarwal@gsk.com

Affiliation Computational Biology, GSK, Collegeville, PA, United States of America
⨯

Systematic interrogation of diverse Omic data reveals interpretable, robust, and generalizable transcriptomic features of clinically successful therapeutic targets

Andrew D. Rouillard,
Mark R. Hurle,
Pankaj Agarwal

Published: May 21, 2018
https://doi.org/10.1371/journal.pcbi.1006142
See the preprint

About the Authors

Andrew D. Rouillard

Roles Data curation, Formal analysis, Methodology, Software, Visualization, Writing – original draft, Writing – review & editing

Affiliation Computational Biology, GSK, Collegeville, PA, United States of America

http://orcid.org/0000-0002-3502-7161

Mark R. Hurle

Roles Conceptualization, Data curation, Methodology, Writing – review & editing

Affiliation Computational Biology, GSK, Collegeville, PA, United States of America

Pankaj Agarwal

Roles Conceptualization, Methodology, Supervision, Writing – review & editing

* E-mail: pankaj.agarwal@gsk.com

Affiliation Computational Biology, GSK, Collegeville, PA, United States of America

Competing Interests

I have read the journal's policy and the authors of this manuscript have the following competing interests: The authors are employed by GlaxoSmithKline. This does not alter our adherence to all PLOS Computational Biology policies on sharing data and materials.

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Phase III clinical investigation
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Gene expression
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Permutation
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Machine learning
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Phenotypes
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Protein expression
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Tissue proteins
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Gene targeting
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