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首页> 外文会议>Computational Intelligence in Bioinformatics and Computational Biology, 2009. CIBCB '09 >A framework for the application of decision trees to the analysis of SNPs data
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A framework for the application of decision trees to the analysis of SNPs data

机译：将决策树应用于SNP数据分析的框架

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摘要

Data mining is the analysis of experimental datasets to extract trends and relationships that can be meaningful for the user. In genetic studies these techniques have revealed interesting findings, especially in the heritable predisposition to contract specific diseases. One of these diseases which is still under extensive analysis is pre-eclampsia, a progressive disorder which occurs during pregnancy and soon after the birth, affecting both the mothers and their babies. There are many choices to be made in the application of the various data mining techniques that may be used to study general genotype-phenotype associations. The aim of this paper is to describe the general framework that we adopted in the application of decision tree algorithms to the analysis of SNPs data related to cases of pre-eclampsia. The results show the validity of this methodology to detect a subset of attributes associated with the predictable variable, providing a reduction in the size of the dataset. Moreover, from the clinical point of view, it confirmed the medical interpretation of the dasiacorrected birth-weight centilepsila (CBC) value of 10 being a meaningful cut-off and confirmed association between an infant's CBC and the dasiaweek of deliverypsila parameter. We hope that the generic framework described here will be of use to other researchers analysing such data.
机译：数据挖掘是对实验数据集的分析，以提取对用户有意义的趋势和关系。在遗传研究中，这些技术揭示了有趣的发现，尤其是在遗传易感性以感染特定疾病方面。子痫前症是一种仍在广泛分析中的疾病，它是一种进行性疾病，发生在怀孕期间和出生后不久，对母亲及其婴儿都有影响。在应用各种数据挖掘技术时，有许多选择可以用来研究一般的基因型-表型关联。本文的目的是描述我们在决策树算法的应用中采用的通用框架，以分析与先兆子痫相关的SNP数据。结果表明，该方法可有效地检测与可预测变量相关联的属性子集，从而减小数据集的大小。此外，从临床角度来看，它证实了医学上的解释，即dasia校正的出生体重百分位数（CBC）值为10是有意义的临界值，并证实了婴儿的CBC与分娩dasiaweek之间的关联。我们希望这里描述的通用框架将对其他分析此类数据的研究人员有用。

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