结肠癌患者诊断的基因标志物识别算法<sup>*</sup>

doi:10.16451/j.cnki.issn1003-6059.201711007

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Abstract To detect those few informative genes with strong classification information and identify colon cancer patients as correctly as possible, an algorithm is proposed in this paper to identify the gene markers for detecting colon cancer patients. The densities and distances are defined for genes firstly. All genes are scattered in a 2D space with gene density and distance as X-axis and Y-axis, respectively. Those genes at high density peaks are selected to construct the optimal gene subset. Then, those samples only with genes in the optimal gene subset of colon dataset are clustered by DP_K-medoids clustering algorithm. The distances between genes or samples are calculated via Euclidean distance, Manhattan distance, Chebyshev distance and the cosine distance, respectively. The experimental results demonstrate that the proposed algorithm can find the optimal gene subset of colon cancer with high accuracy, sensitivity, specificity and MCC, and with a very few number of genes as well.

Key words： Clustering K-medoids Algorithm Density Peak Optimized K-medoids Algorithm Gene Selection Colon Cancer

Received: 04 May 2017

ZTFLH:

TP 181.1

Fund:Supported by National Natural Science Foundation of China(No.61673251), Fundamental Research Funds for the Central Universities(No.GK201701006), Key Science and Technology Program of Shaanxi Province(No.2013K12-03-24), the Innovation Funds of Graduate Programs of Shaanxi Normal University(No.2016CSY009,2015CXS028)

About author:: 谢娟英(通讯作者),女,1971年生,博士,副教授,主要研究方向为机器学习、数据挖掘、生物医学数据分析.E-mail:xiejuany@snnu.edu.cn.
樊雯,女,1994年生,硕士研究生,主要研究方向为数据挖掘.E-mail:fwfanwen@qq.com.

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XIE Juanying,FAN Wen. Gene Markers Identification Algorithm for Detecting Colon Cancer Patients[J]. , 2017, 30(11): 1019-1029.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.201711007 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2017/V30/I11/1019

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