Prediction of drug synergy score using ensemble based differential evolution

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.Inspec keywords: cancer, evolutionary computation, support vector machines, regression analysis, drugs, learning (artificial intelligence), medical computingOther keywords: ensemble based differential evolution, specific cancer agents, efficient regression‐based machine learning technique, drug synergy prediction errors, efficient machine learning technique, drug synergy prediction technique, support vector machine, prediction precision, trial vector generation techniques, initial generation technique, drug synergy data, drug synergy score prediction, medical field, SVM kernel attributes, ensemble based DE, control attribute settings, competitive machine learning techniques, root mean square error