Sequential Support Vector Regression with Embedded Entropy for SNP Selection and Disease Classification.

IF 2.1 4区数学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Statistical Analysis and Data Mining Pub Date : 2011-06-01 DOI:10.1002/sam.10110

Yulan Liang, Arpad Kelemen

引用次数: 6

Abstract

Comprehensive evaluation of common genetic variations through association of 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 paper, we present a sequential support vector 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, logistic regression, CART, and logic regression based SNP selections for disease classification.

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嵌入熵的序列支持向量回归用于SNP选择和疾病分类。

在全基因组尺度上通过SNP结构与常见疾病的关联来综合评价常见遗传变异是目前人类基因组研究的热点。为了更低成本和更快的诊断，需要先进的计算方法来选择具有最高预测精度的最小snp，用于常见的复杂疾病。在本文中，我们提出了一种嵌入熵算法的序列支持向量回归模型来处理选择具有最佳疾病预测性能的snp的冗余问题。我们将提出的方法用于SNP选择和疾病分类，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、逻辑回归、CART和基于逻辑回归的SNP选择方法进行疾病分类。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Statistical Analysis and Data Mining COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCEC-COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

CiteScore

3.20

自引率

7.70%

发文量

期刊介绍： Statistical Analysis and Data Mining addresses the broad area of data analysis, including statistical approaches, machine learning, data mining, and applications. Topics include statistical and computational approaches for analyzing massive and complex datasets, novel statistical and/or machine learning methods and theory, and state-of-the-art applications with high impact. Of special interest are articles that describe innovative analytical techniques, and discuss their application to real problems, in such a way that they are accessible and beneficial to domain experts across science, engineering, and commerce. The focus of the journal is on papers which satisfy one or more of the following criteria: Solve data analysis problems associated with massive, complex datasets Develop innovative statistical approaches, machine learning algorithms, or methods integrating ideas across disciplines, e.g., statistics, computer science, electrical engineering, operation research. Formulate and solve high-impact real-world problems which challenge existing paradigms via new statistical and/or computational models Provide survey to prominent research topics.