【佳学基因检测】使用遗传算法识别生物标记基因的癌症分类

全国十大基因检测公司排名简介

研讨肿瘤基因组学个性化药物选择《肿瘤致病基因突变位点的性质及影响分析》《J Healthc Eng》在. 2022 Feb 22;2022:5821938.发表了一篇题目为《使用遗传算法识别生物标记基因的癌症分类》肿瘤靶向药物治疗基因检测临床研究文章。该研究由M Sathya, M Jeyaselvi, Shubham Joshi, Ekta Pandey, Piyush Kumar Pareek, Sajjad Shaukat Jamal, Vinay Kumar, Henry Kwame Atiglah等完成。促进了肿瘤的正确治疗与个性化用药的发展，进一步强调了基因信息检测与分析的重要性。

肿瘤靶向药物及正确治疗临床研究内容关键词：

肿瘤靶向治疗基因检测临床应用结果

在微阵列基因表达数据中，有大量基因以不同的表达水平表达。鉴于只有几个至关重要的基因，分析和分类跨越整个基因空间的数据集具有挑战性。为了帮助诊断癌症疾病并因此建议个体化治疗，生物标志物基因的发现是必不可少的。从大量候选者开始，并行化贼小冗余和贼大相关性集成（mRMRe）用于从大量候选者中选择前 m 个信息基因。遗传算法 (GA) 用于通过应用马氏距离 (MD) 作为距离度量来启发式地计算理想的基因组。一旦确定了基因，它们就会被输入到遗传算法中。它被用作使用批准的基因解码基因检测的研究方法 (mRMRe-GA) 的四个微阵列数据集的分类器，支持向量机 (SVM) 作为分类基础。 Leave-One-Out-Cross-Validation (LOOCV) 是一种用于评估分类器性能的交叉验证技术。现在正在研究提出的 mRMRe-GA 策略是否可以与其他基因解码基因检测的研究方法进行比较。已经表明，所提出的 mRMRe-GA 基因解码基因检测的研究方法提高了分类正确性，同时比以前的基因解码基因检测的研究方法使用更少的遗传物质。微阵列、基因表达数据、遗传算法、特征选择、支持向量机和癌症分类是本文中使用的一些术语。

肿瘤发生与反复转移国际数据库描述：

In the microarray gene expression data, there are a large number of genes that are expressed at varying levels of expression. Given that there are only a few critically significant genes, it is challenging to analyze and categorize datasets that span the whole gene space. In order to aid in the diagnosis of cancer disease and, as a consequence, the suggestion of individualized treatment, the discovery of biomarker genes is essential. Starting with a large pool of candidates, the parallelized minimal redundancy and maximum relevance ensemble (mRMRe) is used to choose the top m informative genes from a huge pool of candidates. A Genetic Algorithm (GA) is used to heuristically compute the ideal set of genes by applying the Mahalanobis Distance (MD) as a distance metric. Once the genes have been identified, they are input into the GA. It is used as a classifier to four microarray datasets using the approved approach (mRMRe-GA), with the Support Vector Machine (SVM) serving as the classification basis. Leave-One-Out-Cross-Validation (LOOCV) is a cross-validation technique for assessing the performance of a classifier. It is now being investigated if the proposed mRMRe-GA strategy can be compared to other approaches. It has been shown that the proposed mRMRe-GA approach enhances classification accuracy while employing less genetic material than previous methods. Microarray, Gene Expression Data, GA, Feature Selection, SVM, and Cancer Classification are some of the terms used in this paper.