【摘要】 提出了一种新颖的复杂疾病驱使的融合SDA-SVM(Stepwise Discriminant Analysis-Support Vector Machine,SDA-SVM)技术的集成基因挖掘方法。该集成方法融合逐步判别分析和支持向量机的优点,能够有效地进行复杂疾病相关基因的深度挖掘,使得挖掘出的基因能够较好地识别疾病类型和亚型。通过将该方法应用于一套弥散性大B细胞淋巴瘤DNA表达谱数据,并与其它基因挖掘方法对比,结果表明该方法挖掘出的基因具有较高的疾病相关性和较强的疾病类型识别能力。更多还原

【Abstract】 This paper proposes a novel ensemble approach which combines the merits of stepwise discriminant analysis and support vector machine for extracting disease relevant genes and for classifying biological types.By comparing the proposed method with other gene mining methods using a public dataset of the diffuse large b-cell lymphoma,the result demonstrates that the genes extracted by the proposed method have higher disease relevancy and also yield better classification performance.更多还原