Comprehensive evaluation of common genetic variations through association of single nucleotide polymorphism (SNP) structure with common diseases on the genome‐wide scale is currently a hot area in human genome research. For less costly and faster diagnostics, advanced computational approaches are needed to select the minimum SNPs with the highest prediction accuracy for common complex diseases. In this article, we present a sequential support vector (SV) regression model with embedded entropy algorithm to deal with the redundancy for the selection of the SNPs that have best prediction performance of diseases. We implemented our proposed method for both SNP selection and disease classification, and applied it to simulation data sets and two real disease data sets. Results show that on the average, our proposed method outperforms the well‐known methods of support vector machine recursive feature elimination (SVMRFE), logistic regression, classification and regression tree (CART), and logic regression‐based SNP selections for disease classification. © 2011 Wiley Periodicals, Inc. Statistical Analysis and Data Mining 2011

中文翻译：

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用于 SNP 选择和疾病分类的具有嵌入熵的顺序支持向量回归。

在全基因组范围内通过单核苷酸多态性（SNP）结构与常见疾病的关联来综合评估常见遗传变异是目前人类基因组研究的热点。为了降低成本和加快诊断速度，需要先进的计算方法来选择对常见复杂疾病具有最高预测准确性的最小 SNP。在本文中，我们提出了一个带有嵌入式熵算法的序列支持向量 (SV) 回归模型来处理冗余，以选择具有最佳疾病预测性能的 SNP。我们实现了我们提出的 SNP 选择和疾病分类方法，并将其应用于模拟数据集和两个真实疾病数据集。结果表明，平均而言，我们提出的方法优于众所周知的支持向量机递归特征消除 (SVMRFE)、逻辑回归、分类和回归树 (CART) 以及基于逻辑回归的疾病分类 SNP 选择方法。© 2011 Wiley Periodicals, Inc. 统计分析和数据挖掘 2011