Abstract:
:An efficient and novel modeling approach is proposed in this paper for identifying proteins or genes involved in melanoma skin cancer. Two types of classifiers are modeled, based on the chemical structure and hydropathy property of amino acids. These classifiers are further implemented using NI LabVIEW-based hardware kit to observe the real-time response for proper diagnosis. The phase responses, pole-zero diagrams, and transient responses are examined to screen out the genes related to melanoma from healthy genes. The performance of the proposed classifier is measured using various performance measurement metrics in terms of accuracy, sensitivity, specificity, etc. The classifier is experimented along with a color code scheme on skin genes and illustrates the superiority in comparison with traditional methods by achieving 94% of classification accuracy with 96% of sensitivity.Graphical abstract An equivalent electrical model is developed for designing melanoma classifier. Initially, each amino acid is modeled using the RC passive circuit depending on their physicochemical structure and hydropathy nature, to form a gene structure model. The melanoma-related genes are detected by phase, transient, and color code analysis.
journal_name
Med Biol Eng Computjournal_title
Medical & biological engineering & computingauthors
Roy T,Bhattacharjee Pdoi
10.1007/s11517-020-02241-6subject
Has Abstractpub_date
2020-10-01 00:00:00pages
2443-2454issue
10eissn
0140-0118issn
1741-0444pii
10.1007/s11517-020-02241-6journal_volume
58pub_type
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