Microarray data are often extremely asymmetric in dimensionality, such as thousands or even tens of thousands of genes but only a few hundreds of samples or less. Such extreme asymmetry between the dimensionality of g...Microarray data are often extremely asymmetric in dimensionality, such as thousands 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 accuracy. In this paper, a simple modified ant colony optimization （ACO） algorithm is proposed to select tumorelated marker genes, and support vector machine （SVM） is used as classifier to evaluate the performance of the extracted gene subset. Experimental 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展开更多
基金partially supported by National Natural Science Foundation of China (Grant No.60873036)China Postdoctoral Science Foundation(Grant No. 20060400809) Science and Technology Special Foundation for Young Researchers of Hei-longjiang Province of China (Grant No. QC06C022)
文摘Microarray data are often extremely asymmetric in dimensionality, such as thousands 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 accuracy. In this paper, a simple modified ant colony optimization （ACO） algorithm is proposed to select tumorelated marker genes, and support vector machine （SVM） is used as classifier to evaluate the performance of the extracted gene subset. Experimental 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
