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.

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。

图形概要

开发了等效的电模型用于设计黑色素瘤分类器。最初，根据氨基酸的理化结构和亲水性，使用RC无源电路对每种氨基酸进行建模，以形成基因结构模型。通过阶段，瞬时和颜色代码分析检测与黑素瘤相关的基因。