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• W2955244377 abstract "Abstract Several machine learning approaches have been proposed for predicting new benefits of the existing drugs. Although these methods have introduced new usage(s) of some medications, efficient methods can lead to more accurate predictions. To this end, we proposed a novel machine learning method which is based on a new optimization algorithm, named Trader . To show the capabilities of the proposed algorithm which can be applied to the different scope of science, it was compared with ten other state-of-the-art optimization algorithms based on the standard and advanced benchmark functions. Next, a multi-layer artificial neural network was designed and trained by Trader to predict drug-target interactions (DTIs). Finally, the functionality of the proposed method was investigated on some DTIs datasets and compared with other methods. The data obtained by Trader showed that it eliminates the disadvantages of different optimization algorithms, resulting in a better outcome. Further, the proposed machine learning method was found to achieve a significant level of performance compared to the other popular and efficient approaches in predicting unknown DTIs. All the implemented source codes are freely available at https://github.com/LBBSoft/Trader ." @default.
• W2955244377 created "2019-07-12" @default.
• W2955244377 creator A5026902416 @default.
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• W2955244377 creator A5031590552 @default.
• W2955244377 date "2019-06-27" @default.
• W2955244377 modified "2023-10-13" @default.
• W2955244377 title "Trader as a new optimization algorithm predicts drug-target interactions efficiently" @default.
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• W2955244377 doi "https://doi.org/10.1038/s41598-019-45814-8" @default.
• W2955244377 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/6597553" @default.
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• W2955244377 hasPublicationYear "2019" @default.
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