基于广义模糊精度的复杂数量性状基因-基因相互作用识别方法

Xiangdong Zhou, Keith C. C. Chan
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引用次数: 2

摘要

多因子降维(Multifactor dimensionality reduction, MDR)最初被提出用于识别与二元性状相关的基因-基因和基因-环境相互作用。一些研究已将其扩展到数量性状和序数性状。然而,这些方法仍然不是计算效率或有效的。本文提出了一种基于模糊数量性状的有序MDR (QOMDR)方法,该方法首先将数量性状转化为有序性状,然后利用基于模糊集广义成员函数的模糊平衡精度度量来选择与性状相关性强的最佳snp集,从而加强对与数量性状相关的基因-基因相互作用的识别。在两个真实数据集上的实验结果表明,该算法在识别与qt相关的基因-基因相互作用方面具有较好的一致性和分类精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An effective approach to identify gene-gene interactions for complex quantitative traits using generalized fuzzy accuracy
Multifactor dimensionality reduction (MDR) is originally proposed to identify gene-gene and gene-environment interactions associated with binary traits. Some efforts have been made to extend it to quantitative traits (QTs) and ordinal traits. However these methods are still not computationally efficient or effective. In this paper, we propose Fuzzy Quantitative trait based Ordinal MDR (QOMDR) to strengthen identification of gene-gene interactions associated with a quantitative trait by first transforming it to an ordinal trait and then using a fuzzy balance accuracy measure based on generalized member function of fuzzy sets to select best sets of SNPs as having strong association with the trait. Experimental results on two real datasets show that our algorithm has better consistency and classification accuracy in identifying gene-gene interactions associated with QTs.
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