ArticleIET systems biology2019
Prediction of drug synergy score using ensemble based differential evolution.
Article in IET systems biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed.
- Enhanced Cytotoxicity against a Pancreatic Cancer Cell Line Combining Radiation and Gold Nanoparticles.Pharmaceutics · 2024Article
- Dual blockade of BRD4 and ATR/WEE1 pathways exploitsResearch square · 2023Article
- PRODeepSyn: predicting anticancer synergistic drug combinations by embedding cell lines with protein-protein interaction network.Briefings in bioinformatics · 2022Article
- Chaotic emperor penguin optimised extreme learning machine for microarray cancer classification.IET systems biology · 2020Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Prediction of drug synergy score is an ill-posed problem. It plays an efficient role in the medical field for inhibiting specific cancer agents. An efficient regression-based machine learning technique has an ability to minimise the drug synergy prediction errors. Therefore, in this study, an efficient machine learning technique for drug synergy prediction technique is designed by using ensemble based differential evolution (DE) for optimising the support vector machine (SVM). Because the tuning of the attributes of SVM kernel regulates the prediction precision. The ensemble based DE employs two trial vector generation techniques and two control attributes settings. The initial generation technique has the best solution and the other is without the best solution. The proposed and existing competitive machine learning techniques are applied to drug synergy data. The extensive analysis demonstrates that the proposed technique outperforms others in terms of accuracy, root mean square error and coefficient of correlation.
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Registered trials
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