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Evolutionary based drug synergy prediction using adaptive Lévy based neural network structure

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Abstract

As cancer cases are looming large worldwide, the applications of data science and machine learning in these fields possess a greater scope especially when there is the availability of data containing the drug synergy score of different combinations. Predictive analytics holds the ability to generate a more efficient and accurate drug synergy score, that describes the synergies between interactions of two drugs. Various machine learning techniques have been designed so far for the prediction of drug synergy scores. However, parameter tuning is the primary issue for these techniques. To overcome this issue, an efficient and robust Evolutionary based Neural Network Structure using Adaptive Lévy technique has been proposed. The existing machine learning techniques and this evolutionary-based technique are tested on the drug dataset to predict synergy score. As supported by comparative analysis, the findings of the proposed research outperform all the known techniques in terms of parameters like accuracy, coefficient of correlation, and RMSE.

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Acknowledgements

Our research gratefully acknowledge the support of NVIDIA Corporation with the donation of the Quadro P6000 GPU used for this research.

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Authors and Affiliations

  1. Computer Science and Engineering Department, Thapar Institute of Engineering and Technology, Patiala, 147004, Punjab, India

    Harpreet Singh & Prashant Singh Rana

  2. Electronics and Communication Engineering Department, Thapar Institute of Engineering and Technology, Patiala, 147004, Punjab, India

    Urvinder Singh

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  1. Harpreet Singh
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  2. Prashant Singh Rana
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  3. Urvinder Singh
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Correspondence to Harpreet Singh.

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Singh, H., Rana, P.S. & Singh, U. Evolutionary based drug synergy prediction using adaptive Lévy based neural network structure. Multimed Tools Appl 82, 40105–40127 (2023). https://doi.org/10.1007/s11042-023-14536-5

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