Application of a spatially-weighted Relief algorithm for ranking genetic predictors of disease.
Application of a spatially-weighted Relief algorithm for ranking genetic predictors of disease.
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DOI：
10.1186/1756-0381-5-20
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发表时间：
2012-12-03
期刊：
影响因子：
4.5
通讯作者：
Visweswaran S
中科院分区：
文献类型：
--
作者：
Stokes ME;Visweswaran S
鉴定与疾病相关的遗传变异是阐明疾病遗传原因的重要目标。与常见疾病相关的遗传模式很复杂，可能涉及多种相互作用的遗传变异。Relief系列算法是一种有效识别与疾病相关的遗传变异的强大工具，即使这些变异具有非线性相互作用而没有显著的主效应。在过去的二十年里，Relief的许多变体已经开发出来，其中一些已经应用于单核苷酸多态性（SNP）数据。我们开发了一种新的空间加权变化的救济称为Sigmoid加权ReliefF星星（SWRF*），并将其应用于合成SNP数据。当与ReliefF和SURF*（这两种算法已应用于SNP数据以识别相互作用）相比时，SWRF* 具有显著更大的功效。此外，我们开发了一个称为模块化救济框架（MoRF）的框架，可用于开发救济算法的新变体，我们使用MoRF开发SWRF* 算法。MoRF允许通过为组件项指定不同的可互换函数来轻松开发新的Relief算法。使用MORF，我们开发了一种名为SWRF* 的新Relief算法，与现有Relief算法相比，该算法在识别合成数据中相互作用的遗传变异方面具有更强的能力。
Identification of genetic variants that are associated with disease is an important goal in elucidating the genetic causes of diseases. The genetic patterns that are associated with common diseases are complex and may involve multiple interacting genetic variants. The Relief family of algorithms is a powerful tool for efficiently identifying genetic variants that are associated with disease, even if the variants have nonlinear interactions without significant main effects. Many variations of Relief have been developed over the past two decades and several of them have been applied to single nucleotide polymorphism (SNP) data. We developed a new spatially weighted variation of Relief called Sigmoid Weighted ReliefF Star (SWRF*), and applied it to synthetic SNP data. When compared to ReliefF and SURF*, which are two algorithms that have been applied to SNP data for identifying interactions, SWRF* had significantly greater power. Furthermore, we developed a framework called the Modular Relief Framework (MoRF) that can be used to develop novel variations of the Relief algorithm, and we used MoRF to develop the SWRF* algorithm. MoRF allows easy development of new Relief algorithms by specifying different interchangeable functions for the component terms. Using MORF, we developed a new Relief algorithm called SWRF* that had greater ability to identify interacting genetic variants in synthetic data compared to existing Relief algorithms.
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影响因子：
7.5
作者：
Robnik-Sikonja, M;Kononenko, I
通讯作者：
Kononenko, I
DOI：
10.1109/tpami.2007.1093
发表时间：
2007-06-01
影响因子：
23.6
作者：
Sun, Yijun
通讯作者：
Sun, Yijun
影响因子：
4.5
作者：
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通讯作者：
Moore JH
影响因子：
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作者：
De Lobel, Lizzy;Geurts, Pierre;Van Steen, Kristel
通讯作者：
Van Steen, Kristel