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Application of network link prediction in drug discovery

Abbas, K, Abbasi, A, Dong, S, Niu, L, Yu, L, Chen, B, Cai, SM and Hasan, Qambar 2021, Application of network link prediction in drug discovery, BMC bioinformatics, vol. 22, no. 1, pp. 1-21, doi: 10.1186/s12859-021-04082-y.

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Title Application of network link prediction in drug discovery
Author(s) Abbas, K
Abbasi, A
Dong, S
Niu, L
Yu, L
Chen, B
Cai, SM
Hasan, Qambar
Journal name BMC bioinformatics
Volume number 22
Issue number 1
Article ID 187
Start page 1
End page 21
Total pages 21
Publisher Springer
Place of publication Berlin, Germany
Publication date 2021-04-12
ISSN 1471-2105
Keyword(s) Data-driven drug discovery
Drug-target prediction
Network link prediction
Poly-pharmacy side effects prediction
Science & Technology
Life Sciences & Biomedicine
Biochemical Research Methods
Biotechnology & Applied Microbiology
Mathematical & Computational Biology
Biochemistry & Molecular Biology
Summary BackgroundTechnological and research advances have produced large volumes of biomedical data. When represented as a network (graph), these data become useful for modeling entities and interactions in biological and similar complex systems. In the field of network biology and network medicine, there is a particular interest in predicting results from drug–drug, drug–disease, and protein–protein interactions to advance the speed of drug discovery. Existing data and modern computational methods allow to identify potentially beneficial and harmful interactions, and therefore, narrow drug trials ahead of actual clinical trials. Such automated data-driven investigation relies on machine learning techniques. However, traditional machine learning approaches require extensive preprocessing of the data that makes them impractical for large datasets. This study presents wide range of machine learning methods for predicting outcomes from biomedical interactions and evaluates the performance of the traditional methods with more recent network-based approaches.ResultsWe applied a wide range of 32 different network-based machine learning models to five commonly available biomedical datasets, and evaluated their performance based on three important evaluations metrics namely AUROC, AUPR, and F1-score. We achieved this by converting link prediction problem as binary classification problem. In order to achieve this we have considered the existing links as positive example and randomly sampled negative examples from non-existant set. After experimental evaluation we found that Prone, ACT and πΏπ‘…π‘Š5are the top 3 best performers on all five datasets.ConclusionsThis work presents a comparative evaluation of network-based machine learning algorithms for predicting network links, with applications in the prediction of drug-target and drug–drug interactions, and applied well known network-based machine learning methods. Our work is helpful in guiding researchers in the appropriate selection of machine learning methods for pharmaceutical tasks.
Language eng
DOI 10.1186/s12859-021-04082-y
Indigenous content off
Field of Research 01 Mathematical Sciences
06 Biological Sciences
08 Information and Computing Sciences
HERDC Research category C1 Refereed article in a scholarly journal
Free to Read? Yes
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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact