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Classification of Homo sapiens gene behavior using linear discriminant analysis fused with minimum entropy mapping

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Abstract

Classification of Homo sapiens gene behavior employing computational biology is a recent research trend. But monitoring gene activity profile and genetic behavior from the alphabetic DNA sequence using a non-invasive method is a tremendous challenge in functional genomics. The present paper addresses such issue and attempts to differentiate Homo sapiens genes using linear discriminant analysis (LDA) method. Annotated protein coding sequences of Homo sapiens genes, collected from NCBI, are taken as test samples. Minimum entropy-based mapping (MEM) technique assists to extract highest information from the numerical DNA sequences. The proposed LDA technique has successfully classified Homo sapiens genes based on the following features: composition of hydrophilic amino acids, dominance of arginine amino acid, and magnitude and size of individual amino acids. The proposed algorithm is successfully tested on 84 Homo sapiens healthy and cancer genes of the prostate and breast cells. Classification performance of the proposed LDA technique is judged by sensitivity (89.12%), specificity (91.9%), accuracy (90.87%), F1 score (92.03%), Matthews’ correlation coefficients (81.04%), and miss rate (9.12%), and it outperforms other four existing classifiers. The results are cross-validated through Rayleigh PDF and mutual information technique. Fisher test, 2-sample T-test, and relative entropy test are considered to verify the efficacy of the present classifier.

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Funding

The author J. Das would like to thank University Grants Commission (UGC), India, for providing her scholarship (No. F1-17.1/2014-15/RGNF-2014-15-SC-WES-72001).

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  1. Institute of Radio Physics & Electronics, University of Calcutta, Kolkata, India

    Joyshri Das & Soma Barman (Mandal)

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Das, J., Barman (Mandal), S. Classification of Homo sapiens gene behavior using linear discriminant analysis fused with minimum entropy mapping. Med Biol Eng Comput 59, 673–691 (2021). https://doi.org/10.1007/s11517-021-02324-y

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