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一种基于两种距离函数的用于MEC模型的聚类算法。

A clustering algorithm based on two distance functions for MEC model.

作者信息

Wang Ying, Feng Enmin, Wang Ruisheng

机构信息

Department of Applied Mathematics, Dalian University of Technology, Dalian 116024, China.

出版信息

Comput Biol Chem. 2007 Apr;31(2):148-50. doi: 10.1016/j.compbiolchem.2007.02.001. Epub 2007 Feb 9.

Abstract

Haplotype reconstruction, based on aligned single nucleotide polymorphism (SNP) fragments, is to infer a pair of haplotypes from localized polymorphism data gathered through short genome fragment assembly. This paper first presents two distance functions, which are used to measure the difference degree and similarity degree between SNP fragments. Based on the two distance functions, a clustering algorithm is proposed in order to solve MEC model. The algorithm involves two sections. One is to determine the initial haplotype pair, the other concerns with inferring true haplotype pair by re-clustering. The comparison results prove that our algorithm utilizing two distance functions is effective and feasible.

摘要

基于比对的单核苷酸多态性(SNP)片段进行单倍型重建,是从通过短基因组片段组装收集的局部多态性数据中推断出一对单倍型。本文首先提出了两个距离函数,用于测量SNP片段之间的差异程度和相似程度。基于这两个距离函数,提出了一种聚类算法以求解MEC模型。该算法包括两个部分。一是确定初始单倍型对,另一个是通过重新聚类推断真实单倍型对。比较结果证明,我们利用两个距离函数的算法是有效且可行的。

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