A Modified Ant Colony Optimization Algorithm for Tumor Marker Gene Selection

Microarray data are often extremely asymmetric in dimensionality,such as thou-sands or even tens of thousands of genes but only a few hundreds of samples or less.Such extreme asymmetry between the dimensionality of genes and samples can lead to inaccurate diagnosis of disease in clinic.Therefore,it has been shown that selecting a small set of marker genes can lead to improved classification accu-racy.In this paper,a simple modified ant colony optimization (ACO) algorithm is proposed to select tumor-related marker genes,and support vector machine (SVM) is used as classifier to evaluate the performance of the extracted gene subset.Ex-perimental results on several benchmark tumor microarray datasets showed that the proposed approach produces better recognition with fewer marker genes than many other methods.It has been demonstrated that the modified ACO is a useful tool for selecting marker genes and mining high dimension data.

作 者： Hualong Yu Guochang Gu Haibo Liu Jing Shen Jing Zhao   作者單位： College of Computer Science and Technology,Harbin Engineering University,Harbin 150001,China.  刊 名： 基因組蛋白質(zhì)組與生物信息學(xué)報(bào)（英文版）  英文刊名： GENOMICS、PROTEOMICS & BIOINFORMATICS  年，卷(期)： 2009 7(4)  分類號： Q5  關(guān)鍵詞： microarray data   ant colony optimization   marker gene selection   support vector machine  

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