考虑样本不平衡的模型无关的基因选择方法

在基因表达数据分析中,鉴别基因是后续研究中非常重要的信息基因.有很多研究致力于从基因表达数据中选出信息基因这一挑战性工作,并提出了一些基因选择方法.然而,这些方法(特别是非参数选择方法)都没有考虑不同样本类别中样本大小的不平衡性问题.考虑样本不平衡性和基因选择方法的稳定性,给出一个全新的与数据分布模型无关的基因选择方法.在类内变化小和类间差别大的策略下,选择敏感的度量函数提高方法的鉴别能力,同时,利用类内变化和类间差别的一致性来增加方法的稳定性和适用性.这一方法不但可以应用于两个类别的情况,也可以应用于多个类别的情况.最后,使用两组真实的基因表达数据对所提出的方法进行了验证.实验结果表明,这一方法比其他方法具有更高的有效性和稳健性.

Model-Free Gene Selection Method by Considering Unbalanced Samples

In gene expression data analysis, discriminator genes are importantly informative genes for further research. Recently, a great deal of research has focused on the challenging task of identifying these informative genes from microarray data. However, the sizes of sample classes in microarray data are often unbalanced. The unbalance of samples has not been explicitly and correctly considered by the existing gene selection methods, especially nonparametric methods. Considering the unbalance of samples and the stability of the approach for identifying informative genes, a novel and model-free gene selection method is proposed in this paper. With considering within-class difference and between-class variation, as well as the homogeneities of the within-class difference and between-class variations, scoring functions of genes are constructed to select discriminator genes. This method is not only applicable in two-category case but also applicable in multi-category case. The experimental results on two publicly available microarray datasets, leukemia data and small round blue cell tumor data, show that the proposed method is very efficient and robust to select discriminator genes.