Extraction of informative genes from microarray data
Extraction of informative genes from microarray data
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DOI：
10.1145/1068009.1068081
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发表时间：
2005-06
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影响因子：
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通讯作者：
T. Paul;H. Iba
中科院分区：
文献类型：
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作者：
T. Paul;H. Iba
识别那些可能预测不同类型癌症临床行为的基因是具有挑战性的，因为与大量基因相比，可用的患者样本数量较少，而且微阵列数据的噪声性质。在根据信噪比选择了一些好的基因后，像聚类这样的无监督学习和像k-最近邻(KNN)分类器这样的有监督学习被广泛地应用于癌症研究中，以将癌症的病理行为与癌组织和正常组织中基因表达水平的差异联系起来。通过应用像概率模型构建遗传算法(PMBGA)这样的自适应搜索，可能得到比上述方法更准确地对患者样本进行分类的更小的基因子集。本文提出了一种基于PMBGA的支持向量机作为分类器从微阵列数据中提取信息基因的新方法。我们将我们的方法应用于三个微阵列数据集，并给出了实验结果。与使用KNN作为分类器的基于排名的方法相比，基于支持向量机的方法在这些数据集上获得了令人鼓舞的结果。
Identification of those genes that might anticipate the clinical behavior of different types of cancers is challenging due to availability of a smaller number of patient samples compared to huge number of genes, and the noisy nature of microarray data. After selection of some good genes based on signal-to-noise ratio, unsupervised learning like clustering and supervised learning like k-nearest neighbor (kNN) classifier are widely used in cancer researches to correlate the pathological behavior of cancers with the gene expression levels' differences in cancerous and normal tissues. By applying adaptive searches like Probabilistic Model Building Genetic Algorithm (PMBGA), it may be possible to get a smaller size gene subset that would classify patient samples more accurately than the above methods. In this paper, we propose a new PMBGA based method to extract informative genes from microarray data using Support Vector Machine (SVM) as a classifier. We apply our method to three microarray data sets and present the experimental results. Our method with SVM obtains encouraging results on those data sets as compared with the rank based method using kNN as a classifier.