Medical & Biological Engineering & Computing ( IF 3.2 ) Pub Date : 2020-08-08 , DOI: 10.1007/s11517-020-02241-6 Tanusree Roy 1 , Pranabesh Bhattacharjee 1
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.
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中文翻译:
使用电子建模技术对黑素瘤分类器的性能进行分析。
本文提出了一种有效且新颖的建模方法,用于识别与黑色素瘤皮肤癌有关的蛋白质或基因。基于氨基酸的化学结构和亲水性,对两种类型的分类器进行建模。使用基于NI LabVIEW的硬件套件进一步实现这些分类器,以观察实时响应以进行正确诊断。检查相位响应,零极点图和瞬态响应,以从健康基因中筛选出与黑色素瘤相关的基因。拟议的分类器的性能是根据准确性,敏感性,特异性等方面的各种性能衡量指标来衡量的。
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