Xiong Wei, Cai Zhibin, Ma Jinwen
School of Mathematical Sciences and Laboratory of Mathematics and Applied Mathematics (LMAM), Peking University, Beijing 100871, China.
Genomics Proteomics Bioinformatics. 2008 Jun;6(2):83-90. doi: 10.1016/S1672-0229(08)60023-6.
Microarray data based tumor diagnosis is a very interesting topic in bioinformatics. One of the key problems is the discovery and analysis of informative genes of a tumor. Although there are many elaborate approaches to this problem, it is still difficult to select a reasonable set of informative genes for tumor diagnosis only with microarray data. In this paper, we classify the genes expressed through microarray data into a number of clusters via the distance sensitive rival penalized competitive learning (DSRPCL) algorithm and then detect the informative gene cluster or set with the help of support vector machine (SVM). Moreover, the critical or powerful informative genes can be found through further classifications and detections on the obtained informative gene clusters. It is well demonstrated by experiments on the colon, leukemia, and breast cancer datasets that our proposed DSRPCL-SVM approach leads to a reasonable selection of informative genes for tumor diagnosis.
基于微阵列数据的肿瘤诊断是生物信息学中一个非常有趣的课题。关键问题之一是肿瘤信息基因的发现与分析。尽管针对这个问题有许多精细的方法，但仅利用微阵列数据为肿瘤诊断选择一组合理的信息基因仍然很困难。在本文中，我们通过距离敏感竞争惩罚学习（DSRPCL）算法将通过微阵列数据表达的基因分类为若干簇，然后借助支持向量机（SVM）检测信息基因簇或集合。此外，通过对获得的信息基因簇进行进一步的分类和检测，可以找到关键或强大的信息基因。在结肠癌、白血病和乳腺癌数据集上的实验充分证明，我们提出的DSRPCL - SVM方法能够为肿瘤诊断合理选择信息基因。
