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Dual transcriptomic and molecular machine learning predicts all major clinical forms of drug cardiotoxicity

Abstract:

Computational methods can increase productivity of drug discovery pipelines, through overcoming challenges such as cardiotoxicity identification. We demonstrate prediction and preservation of cardiotoxic relationships for six drug-induced cardiotoxicity types using a machine learning approach on a large collected and curated dataset of transcriptional and molecular profiles (1,131 drugs, 35% with known cardiotoxicities, and 9,933 samples). The algorithm generality is demonstrated through vali...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.3389/fphar.2020.00639

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Division:
MPLS
Department:
Computer Science
Sub department:
Computer Science
Role:
Author
Publisher:
Frontiers Media Publisher's website
Journal:
Frontiers in Pharmacology Journal website
Volume:
11
Article number:
639
Publication date:
2020-05-21
Acceptance date:
2020-04-21
DOI:
EISSN:
1663-9812
Language:
English
Keywords:
Subjects:
Pubs id:
1102454
Local pid:
pubs:1102454
Deposit date:
2020-04-30

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