A stable gene selection in microarray data analysis.
A stable gene selection in microarray data analysis.
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DOI：
10.1186/1471-2105-7-228
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发表时间：
2006-04-27
影响因子：
3
通讯作者：
Lin G
中科院分区：
文献类型：
--
作者：
Yang K;Cai Z;Li J;Lin G
微阵列数据分析是臭名昭著的，涉及大量的基因相比，相对较少的样品。基因选择是寻找在不同条件下差异表达最显著的基因，一直是研究的热点。一般来说，一个好的基因选择方法可以显着提高分类性能。基因选择的难点之一是不同条件下的样本数量差异很大。本文提出了两种新的基因选择方法，这两种方法不受不平衡样本类大小的影响，并且不对基因表达值假设任何显式的统计模型。他们在8个公开的微阵列数据集上进行了评估，使用留一交叉验证和5倍交叉验证。性能通过使用基于训练数据集的排名靠前的基因的分类准确率来衡量。实验结果表明，所提出的基因选择方法是有效的，有效的，并在确定差异表达基因的强大。采用现有的基于SVM和基于KNN的分类器，我们提出的方法所选择的基因通常会给出更准确的分类结果，通常当训练数据集中的样本类大小不平衡时。
Microarray data analysis is notorious for involving a huge number of genes compared to a relatively small number of samples. Gene selection is to detect the most significantly differentially expressed genes under different conditions, and it has been a central research focus. In general, a better gene selection method can improve the performance of classification significantly. One of the difficulties in gene selection is that the numbers of samples under different conditions vary a lot. Two novel gene selection methods are proposed in this paper, which are not affected by the unbalanced sample class sizes and do not assume any explicit statistical model on the gene expression values. They were evaluated on eight publicly available microarray datasets, using leave-one-out cross-validation and 5-fold cross-validation. The performance is measured by the classification accuracies using the top ranked genes based on the training datasets. The experimental results showed that the proposed gene selection methods are efficient, effective, and robust in identifying differentially expressed genes. Adopting the existing SVM-based and KNN-based classifiers, the selected genes by our proposed methods in general give more accurate classification results, typically when the sample class sizes in the training dataset are unbalanced.
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