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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Vydáno v:BMC bioinformatics Ročník 20; číslo 1; s. 653 - 12
Hlavní autoři: Mayers, Michael, Li, Tong Shu, Queralt-Rosinach, Núria, Su, Andrew I.
Médium: Journal Article
Jazyk:angličtina
Vydáno: London BioMed Central 11.12.2019
BioMed Central Ltd
Springer Nature B.V
BMC
Témata:
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
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Shrnutí: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 repositioned via computational screening remains low. New strategies for algorithm evaluation that more accurately reflect the repositioning potential of a compound could provide a better target for future optimizations. Results Using a text-mined database, we applied a previously described network-based computational repositioning algorithm, yielding strong results via cross-validation, averaging 0.95 AUROC on test-set indications. However, to better approximate a real-world scenario, we built a time-resolved evaluation framework. At various time points, we built networks corresponding to prior knowledge for use as a training set, and then predicted on a test set comprised of indications that were subsequently described. This framework showed a marked reduction in performance, peaking in performance metrics with the 1985 network at an AUROC of .797. Examining performance reductions due to removal of specific types of relationships highlighted the importance of drug-drug and disease-disease similarity metrics. Using data from future timepoints, we demonstrate that further acquisition of these kinds of data may help improve computational results. Conclusions Evaluating a repositioning algorithm using indications unknown to input network better tunes its ability to find emerging drug indications, rather than finding those which have been randomly withheld. Focusing efforts on improving algorithmic performance in a time-resolved paradigm may further improve computational repositioning predictions.
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ISSN:1471-2105
1471-2105
DOI:10.1186/s12859-019-3297-0