基于基因表达谱的肿瘤亚型识别与分类特征基因选取研究

利用肿瘤基因表达谱建立有效的"预测性"分类模型,对肿瘤的不同亚型进行准确判别并找出决定样本类别的一组特征基因是当前生物信息学研究的重要课题.本文在分析肿瘤基因表达谱特征的基础上,以急性白血病的基因表达谱为例,研究了肿瘤亚型识别与分类特征基因选取问题.在类别可分离性判据的问题上,修正了已有的"信噪比"指标,据此进行无关基因的剔除,并以支持向量机作为分类器进行肿瘤亚型的识别.在特征基因选取问题上,本文从生物学分析出发,首先剔除无关基因和具有较强相关性的冗余基因,然后采用顺序浮动搜索算法进行分类特征基因的选取.实验结果表明了上述方法的可行性和有效性.

Cancer Subtype Recognition and Feature Selection with Gene Expression Profiles

The classification of different cancer subtypes and feature subset selection is of great importance in cancer diagnosis and has recently received a great deal of attention in the field of bioinformatics.The purpose of this study is to develop a method of classifying tumors to specific categories and select a small subset of genes for classification based on gene expression profiles.Firstly,a new metric for class separability was proposed in order to remove the genes irrelevant to the classification task,and then a support vector machine was applied to distinguish different cancer types.The feature subset selection process is performed by pair-wise redundancy analysis and the "sequential floating forward search" method after irrelevant genes have been removed.We analyzed the gene expression profiles of human acute leukemia as a test case,and the results showed the feasibility and effectiveness of the method proposed in this paper.