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Hybrid Sea Lion Crow Search Algorithm-Based Stacked Autoencoder for Drug Sensitivity Prediction From Cancer Cell Lines

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  • Rupali A. Mahajan

    (MIT-ADT University, India)

  • Nilofer Karim Shaikh

    (MIT-ADT University, India)

  • Atharva Balkrishna Tikhe

    (MIT-ADT University, India)

  • Renu Vyas

    (MIT-ADT University, India)

  • Smita M. Chavan

    (Government College of Engineering, India)

Abstract

Providing better therapy to cancer patients remains a major task due to drug resistance of tumor cells. This paper proposes a sea lion crow search algorithm (SLCSA) for drug sensitivity prediction. The drug sensitivity from cultured cell lines is predicted using stacked autoencoder, and the proposed SLCSA is derived from a combination of sea lion optimization (SLnO) and crow search algorithm (CSA). The implemented approach has offered superior results. The maximum value of testing accuracy for normal is 0.920, leukemia is 0.920, NSCLC is 0.912, and urogenital is 0.914.

Suggested Citation

  • Rupali A. Mahajan & Nilofer Karim Shaikh & Atharva Balkrishna Tikhe & Renu Vyas & Smita M. Chavan, 2022. "Hybrid Sea Lion Crow Search Algorithm-Based Stacked Autoencoder for Drug Sensitivity Prediction From Cancer Cell Lines," International Journal of Swarm Intelligence Research (IJSIR), IGI Global, vol. 13(1), pages 1-21, January.
    • Handle: RePEc:igg:jsir00:v:13:y:2022:i:1:p:1-21
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