Open-source chemogenomic data-driven algorithms for predicting drug–target interactions

Abstract While novel technologies such as high-throughput screening have advanced together with significant investment by pharmaceutical companies during the past decades, the success rate for drug development has not yet been improved prompting researchers looking for new strategies of drug discove...

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Veröffentlicht in:	Briefings in bioinformatics Jg. 20; H. 4; S. 1465 - 1474
Hauptverfasser:	Hao, Ming, Bryant, Stephen H, Wang, Yanli
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	England Oxford University Press 19.07.2019 Oxford Publishing Limited (England)
Schlagworte:	Accessibility Algorithms Availability Computational Biology Computer applications Computer Simulation Drug development Drug Development - methods Drug Development - statistics & numerical data Drug discovery Drug Discovery - methods Drug Discovery - statistics & numerical data Drug Repositioning - methods Drug Repositioning - statistics & numerical data Gene sequencing Genomes High-throughput screening Humans Pharmaceutical industry Pharmacogenomic Testing - methods Pharmacogenomic Testing - statistics & numerical data Programming languages R&D Research & development Therapeutic targets Whole genome sequencing drug–target interaction drug discovery mean percentile ranking chemogenomic data open-source code in silico drug repositioning
ISSN:	1467-5463, 1477-4054, 1477-4054
Online-Zugang:	Volltext
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