Xie Juanying, Wang Yuchen, Wu Zhaozhong
School of Computer Science, Shaanxi Normal University, Xi'an, People's Republic of China.
Health Inf Sci Syst. 2019 Oct 14;7(1):23. doi: 10.1007/s13755-019-0085-1. eCollection 2019 Dec.
Detecting the key differential genes of colon cancers is very important to tell colon cancer patients from normal people. A gene selection algorithm for colon cancers is proposed by using the dynamic modeling properties of chameleon algorithm and its capability to discover any arbitrary shape clusters. This chameleon algorithm based gene selection algorithm comprises three steps. The first step is to select those genes with higher Fisher function values as candidate genes. The second step is to detect gene groups by using chameleon algorithm based on Euclidean distance. The third step is to select the most important gene from each gene cluster to comprise the gene subset by using the information index to classification of each gene. After that the chameleon algorithm is used to detect groups of colon cancer patients and normal people only with genes in gene subset. The final clustering accuracy of chameleon algorithm with the selected genes is up to 85.48%. The clustering analysis to colon cancer data and the comparisons to the other related studies demonstrate that the proposed algorithm is effective in detecting the differential genes of colon cancers.
检测结肠癌的关键差异基因对于区分结肠癌患者和正常人非常重要。利用变色龙算法的动态建模特性及其发现任意形状聚类的能力，提出了一种用于结肠癌的基因选择算法。这种基于变色龙算法的基因选择算法包括三个步骤。第一步是选择那些具有较高Fisher函数值的基因作为候选基因。第二步是基于欧几里得距离，使用变色龙算法检测基因组。第三步是通过使用每个基因的分类信息指数，从每个基因簇中选择最重要的基因来组成基因子集。之后，仅使用基因子集中的基因，用变色龙算法检测结肠癌患者组和正常人群体。所选基因的变色龙算法最终聚类准确率高达85.48%。对结肠癌数据的聚类分析以及与其他相关研究的比较表明，该算法在检测结肠癌差异基因方面是有效的。
