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首页> 外文期刊>Bioinformatics >GPDTI: A genetic programming decision tree induction method to find epistatic effects in common complex diseases
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GPDTI: A genetic programming decision tree induction method to find epistatic effects in common complex diseases

机译:GPDTI:一种遗传程序决策树诱导方法,用于发现常见复杂疾病中的上位性效应

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摘要

Motivation: The identification of risk-associated genetic variants in common diseases remains a challenge to the biomedical research community. It has been suggested that common statistical approaches that exclusively measure main effects are often unable to detect interactions between some of these variants. Detecting and interpreting interactions is a challenging open problem from the statistical and computational perspectives. Methods in computing science may improve our understanding on the mechanisms of genetic disease by detecting interactions even in the presence of very low heritabilities. Results: We have implemented a method using Genetic Programming that is able to induce a Decision Tree to detect interactions in genetic variants. This method has a cross-validation strategy for estimating classification and prediction errors and tests for consistencies in the results. To have better estimates, a new consistency measure that takes into account interactions and can be used in a genetic programming environment is proposed. This method detected five different interaction models with heritabilities as low as 0.008 and with prediction errors similar to the generated errors.
机译:动机:常见疾病中与风险相关的遗传变异的鉴定仍然是对生物医学研究界的挑战。已经提出,专门测量主要影响的通用统计方法通常无法检测到其中一些变体之间的相互作用。从统计和计算的角度来看,检测和解释交互是一个具有挑战性的开放性问题。即使在遗传力非常低的情况下,计算机科学方法也可以通过检测相互作用来增进我们对遗传疾病机理的理解。结果:我们使用遗传编程实现了一种方法,该方法能够诱导决策树以检测遗传变异中的相互作用。该方法具有用于估计分类和预测误差的交叉验证策略,并测试结果的一致性。为了获得更好的估计,提出了一种新的一致性度量,该度量考虑了相互作用并且可以在遗传编程环境中使用。该方法检测到五个不同的相互作用模型,其遗传力低至0.008,并且预测误差与所产生的误差相似。

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