Time-resolved evaluation of compound repositioning predictions on a text-mined knowledge network

Background Computational compound repositioning has the potential for identifying new uses for existing drugs, and new algorithms and data source aggregation strategies provide ever-improving results via in silico metrics. However, even with these advances, the number of compounds successfully repos...

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Veröffentlicht in:	BMC bioinformatics Jg. 20; H. 1; S. 653 - 12
Hauptverfasser:	Mayers, Michael, Li, Tong Shu, Queralt-Rosinach, Núria, Su, Andrew I.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London BioMed Central 11.12.2019 BioMed Central Ltd Springer Nature B.V BMC
Schlagworte:	Algorithms Analysis Artificial intelligence Bioinformatics Biomedical and Life Sciences Compound repositioning Computational Biology - methods Computational Biology/Bioinformatics Computer Appl. in Life Sciences Computer applications Computer networks Data Mining Disease Drug central Drug Repositioning Drugs Gene expression Heterogeneous network Humans Knowledge Knowledge Bases Life Sciences Machine Learning Machine Learning and Artificial Intelligence in Bioinformatics Medical Subject Headings-MeSH Microarrays Natural language processing Ontology Performance measurement Predictions Reproducibility of Results Research Article Semantic Medline database Semantic network Semantics Time Factors Unified medical language system Compound repositioning Heterogeneous network Semantic Medline database Semantic network Machine learning Drug central Unified medical language system
ISSN:	1471-2105, 1471-2105
Online-Zugang:	Volltext
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