A comparative study of feature selection and multiclass classification methods for tissue classification based on gene expression
A comparative study of feature selection and multiclass classification methods for tissue classification based on gene expression
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DOI：
10.1093/bioinformatics/bth267
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发表时间：
2004-10-12
期刊：
影响因子：
5.8
通讯作者：
Ogihara, M
中科院分区：
文献类型：
--
作者：
Li, T;Zhang, CL;Ogihara, M
研究了基于基因表达的组织分类多类分类器的构建问题。微阵列技术的最新发展使生物学家能够在一次实验中量化成千上万个基因的基因表达。生物学家已经开始收集大量样本的基因表达。利用微阵列数据的一个紧迫问题是开发基于基因表达的样品表征方法。在研究方向上最基本的一步是二值样本分类，这在过去的几年里得到了广泛的研究。本文研究了下一步——基于基因表达的样本多类分类。表达数据的特点（如基因数量多而样本量小）使得分类问题更具挑战性。构建多类分类器的过程分为两个部分：(i)选择用于训练和测试的特征（即基因）和（ii）选择分类方法。本文比较了不同类型的基因表达数据集的各种特征选择方法和各种最新的分类方法。我们的研究表明，基因表达数据集的多类分类问题比二元分类问题要困难得多。难点在于数据维数高，样本量小。随着分类数量的增加，分类精度似乎会迅速下降。特别是，对于大型数据集（如NCI60和GCM），无论选择何种方法，准确率都非常低。虽然增加样本数量是解决准确性下降问题的可行方案，但重要的是开发能够有效分析这些特殊数据集的多类表达式数据的算法。
This paper studies the problem of building multiclass classifiers for tissue classification based on gene expression. The recent development of microarray technologies has enabled biologists to quantify gene expression of tens of thousands of genes in a single experiment. Biologists have begun collecting gene expression for a large number of samples. One of the urgent issues in the use of microarray data is to develop methods for characterizing samples based on their gene expression. The most basic step in the research direction is binary sample classification, which has been studied extensively over the past few years. This paper investigates the next step-multiclass classification of samples based on gene expression. The characteristics of expression data (e.g. large number of genes with small sample size) makes the classification problem more challenging.The process of building multiclass classifiers is divided into two components: (i) selection of the features (i.e. genes) to be used for training and testing and (ii) selection of the classification method. This paper compares various feature selection methods as well as various state-of-the-art classification methods on various multiclass gene expression datasets.Our study indicates that multiclass classification problem is much more difficult than the binary one for the gene expression datasets. The difficulty lies in the fact that the data are of high dimensionality and that the sample size is small. The classification accuracy appears to degrade very rapidly as the number of classes increases. In particular, the accuracy was very low regardless of the choices of the methods for large-class datasets (e.g. NCI60 and GCM). While increasing the number of samples is a plausible solution to the problem of accuracy degradation, it is important to develop algorithms that are able to analyze effectively multiple-class expression data for these special datasets.