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    ARTICLE

    Regulatory Genes Through Robust-SNR for Binary Classification Within Functional Genomics Experiments

    Muhammad Hamraz1, Dost Muhammad Khan1, Naz Gul1, Amjad Ali1, Zardad Khan1, Shafiq Ahmad2, Mejdal Alqahtani2, Akber Abid Gardezi3, Muhammad Shafiq4,*

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3663-3677, 2023, DOI:10.32604/cmc.2023.030064 - 31 October 2022

    Abstract The current study proposes a novel technique for feature selection by inculcating robustness in the conventional Signal to noise Ratio (SNR). The proposed method utilizes the robust measures of location i.e., the “Median” as well as the measures of variation i.e., “Median absolute deviation (MAD) and Interquartile range (IQR)” in the SNR. By this way, two independent robust signal-to-noise ratios have been proposed. The proposed method selects the most informative genes/features by combining the minimum subset of genes or features obtained via the greedy search approach with top-ranked genes selected through the robust signal-to-noise ratio (RSNR).… More >

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