摘要
基因表达数据存在高维、小样本、高噪声等特性,使得相应的肿瘤分类诊断面临着一定的挑战。为了实现更加精确的分类准确率,利用人工蜂群(artificial bee colony,ABC)算法对支持向量机(support vector machine,SVM)的核函数参数和惩罚因子进行优化,采用准确率作为分类模型的适应度函数,提出一种基于ABC和SVM的基因表达数据分类方法 ABC-SVM。在6种公开的肿瘤基因表达数据集上进行试验,并对比分析其他的分类方法。结果表明,在筛选得到的较少信息基因基础上,ABC-SVM可获得更高的肿瘤分类准确率,对肿瘤样本类型进行更有效的分类预测。
The characteristics of high dimension,small sample and high noise for gene expression data raised many challenges in tumor diagnosis. In order to classify tumor gene expression data more accurately,the kernel function parameters and penalty factors of SVM（ support vector machine） were optimized by ABC（ artificial bee colony） algorithm,in which classification accuracy was used as the fitness function. A new gene expression data classification method based on ABC algorithm and SVM,which named ABC-SVM,was proposed. Experiments were conducted on six public tumor gene expression datasets,and other classicfication methods were compared. The results showed that ABC-SVM,a method based on fewer informative genes,could obtain higher classification accuracy,and the classification of tumor samples could be more effectively predicted.
作者
叶明全
高凌云
万春圆
YE Mingquan;GAO Lingyun;WAN Chunyuan(Research Center of Health Big Data Mining and Applications, Wannan Medical College, Wuhu 241002, Anhui, China)
出处
《山东大学学报（工学版）》
CAS
北大核心
2018年第3期10-16,共7页
Journal of Shandong University（Engineering Science）
基金
国家自然科学基金资助项目(61672386)
安徽省自然科学基金资助项目(1708085MF142)
教育部人文社会科学研究规划基金资助项目(16YJAZH071)
安徽高校省级自然科学研究重点基金资助项目(KJ2014A266)
关键词
人工蜂群
支持向量机
基因表达数据
智能优化
肿瘤分类
生物信息学
artificial bee colony
support vector machine
gene expression data
intelligent optimization
tumor classification
bioinformatics
