Coot–Lion optimized deep learning algorithm for COVID-19 point mutation rate prediction using genome sequences

Praveen Gugulothu and Raju Bhukya

Computer Methods in Biomechanics and Biomedical Engineering, 2024, vol. 27, issue 11, 1410-1429

Abstract: In this study, a deep quantum neural network (DQNN) based on the Lion-based Coot algorithm (LBCA-based Deep QNN) is employed to predict COVID-19. Here, the genome sequences are subjected to feature extraction. The fusion of features is performed using the Bray-Curtis distance and the deep belief network (DBN). Lastly, a deep quantum neural network (Deep QNN) is used to predict COVID-19. The LBCA is obtained by integrating Coot algorithm and LOA. The COVID-19 predictions are done with mutation points. The LBCA-based Deep QNN outperformed with testing accuracy of 0.941, true positive rate of 0.931, and false positive rate of 0.869.

Date: 2024
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DOI: 10.1080/10255842.2023.2244109

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