|本期目录/Table of Contents|

[1]叶小泉,吴云峰*.基于支持向量机递归特征消除和特征聚类的致癌基因选择方法[J].厦门大学学报(自然科学版),2018,57(05):702-707.[doi:10.6043/j.issn.0438-0479.201803022]
 YE Xiaoquan,WU Yunfeng*.Cancer Gene Selection Algorithm Based on Support Vector Machine Recursive Feature Elimination and Feature Clustering[J].Journal of Xiamen University(Natural Science),2018,57(05):702-707.[doi:10.6043/j.issn.0438-0479.201803022]
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《厦门大学学报(自然科学版)》[ISSN:0438-0479/CN:35-1070/N]

卷:
57卷
期数:
2018年05期
页码:
702-707
栏目:
研究论文
出版日期:
2018-09-27

文章信息/Info

Title:
Cancer Gene Selection Algorithm Based on Support Vector Machine Recursive Feature Elimination and Feature Clustering
文章编号:
0438-0479(2018)05-0702-06
作者:
叶小泉吴云峰*
厦门大学 信息科学与技术学院,福建省智慧城市感知与计算重点实验室,福建 厦门 361005
Author(s):
YE XiaoquanWU Yunfeng*
Fujian Key Laboratory of Sensing and Computing for Smart City,School of Information Science and Engineering,Xiamen University,Xiamen 361005,China
关键词:
基因表达谱 特征选择 K均值聚类 支持向量机
Keywords:
gene expression profile feature selection K-means support vector machine
分类号:
TP 391.4
DOI:
10.6043/j.issn.0438-0479.201803022
文献标志码:
A
摘要:
癌症通常由基因发生突变引起,因此从大量基因中有效地识别出少量致癌基因具有重要意义.针对基因表达谱数据高维小样本的特点,将支持向量机递归特征消除(SVM-RFE)和特征聚类算法相结合,提出一种新的基因选择方法:K类别SVM-RFE(K-SVM-RFE).该算法通过特征排序算法去除大量无关基因,利用K均值聚类算法将相似基因聚为一类,并通过两次SVM-RFE算法精选致癌基因.随后将K-SVM-RFE算法应用于多个基因表达谱数据集,并对其中的关键参数设置进行了讨论.实验结果表明K-SVM-RFE算法所选基因较已有方法在分类准确率上有显著提高,特别是在选择少量致癌基因上效果提升更为明显.
Abstract:
Cancer is usually caused by mutations in genes.It is significant to effectively identify a small number of pathogenic genes from numerous genes.Based on characteristics of gene expression profile data,a novel algorithm(K- SVM-RFE)of gene selection is proposed by combining SVM-RFE with feature clustering algorithm.First,irrelevant genes were removed by feature ranking algorithm.Then,these genes were clustered by K-means and the SVM-RFE algorithm was applied twice to select key genes.We conducted experiments on some real-world data sets and discussed the parameter settings in our method.Results show that,compared with the existing methods,genes selected by the K-SVM-RFE algorithm have significantly improved the classification accuracy,especially in selecting a few key genes.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2018-03-08 录用日期:2017-07-16
基金项目:国家自然科学基金(61771331)
*通信作者:yunfengwu@xmu.edu.cn
引文格式:叶小泉,吴云峰.基于支持向量机递归特征消除和特征聚类的致癌基因选择方法[J].厦门大学学报(自然科学版),2018,57(5):702-707.
Citation:YE X Q,WU Y F.Cancer gene selection algorithm based on support vector machine recursive feature elimination and feature clustering[J].Xiamen Univ Nat Sci,2018,57(5):702-707.(in Chinese)
更新日期/Last Update: 1900-01-01