ArticleScientific reports2019
Trader as a new optimization algorithm predicts drug-target interactions efficiently.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- An interpretable deep learning framework uncovers features governing CRISPR-Cas9 genome-editing efficiency.Bioinformatics (Oxford, England) · 2026Article
- Emerging advancements and expanding technological scope of education and practices in pharmacy and pharmaceutical sciences.BioImpacts : BI · 2026Review
- In-silico identification of therapeutic targets in pancreatic ductal adenocarcinoma using WGCNA and Trader.Scientific reports · 2024Article
- Interpretable deep residual network uncovers nucleosome positioning and associated features.Nucleic acids research · 2024Article
- Joint deep autoencoder and subgraph augmentation for inferring microbial responses to drugs.Briefings in bioinformatics · 2023Article
- Advancement of Computational Design Drug Delivery System in COVID-19: Current Updates and Future Crosstalk- A Critical update.Infectious disorders drug targets · 2023Article
- A voting-based machine learning approach for classifying biological and clinical datasets.BMC bioinformatics · 2023Article
- A fuzzy logic-based computational method for the repurposing of drugs against COVID-19.BioImpacts : BI · 2022Article
- Structure-based drug repurposing against COVID-19 and emerging infectious diseases: methods, resources and discoveries.Briefings in bioinformatics · 2021Article
- Distinguishing drug/non-drug-like small molecules in drug discovery using deep belief network.Molecular diversity · 2021Article
- Article
- Synthetic repurposing of drugs against hypertension: a datamining method based on association rules and a novel discrete algorithm.BMC bioinformatics · 2020Article
- Computational-based drug repurposing methods in COVID-19.BioImpacts : BI · 2020Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
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 .
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