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首页> 外文期刊>Journal of Bioinformatics and Computational Biology >Predicting Risk of Coronary Artery Disease from DNA Microarray-Based Genotyping Using Neural Networks and Other Statistical Analysis Tool
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Predicting Risk of Coronary Artery Disease from DNA Microarray-Based Genotyping Using Neural Networks and Other Statistical Analysis Tool

机译：使用神经网络和其他统计分析工具从基于DNA芯片的基因分型预测冠状动脉疾病的风险

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摘要

This paper presents a novel approach for complex disease prediction that we have developed, exemplified by a study on risk of coronary artery disease (CAD). This multi-disciplinary approach straddles fields of microarray technology and genetics, neural networks (NN), data mining and machine learning, as well as traditional statistical analysis techniques, namely principal components analysis (PCA) and factor analysis (FA). A description of the biological background of the study is given, followed by a detailed description of how the problem has been modeled for analyses by neural networks and FA. A committee learning approach for NN has been used to improve generalization rates. We show that our NN approach is able to yield promising prediction results despite using only the most fundamental network structures. More interestingly, through the statistical analysis process, genes of similar biological functions have been clustered. In addition, a gene marker involved in breaking down lipids has been found to be the most correlated to CAD.
机译：本文介绍了我们开发的一种用于复杂疾病预测的新方法，以对冠状动脉疾病（CAD）风险的研究为例。这种跨学科的方法跨越了微阵列技术和遗传学，神经网络（NN），数据挖掘和机器学习以及传统的统计分析技术（即主成分分析（PCA）和因子分析（FA））领域。给出了该研究的生物学背景的描述，然后详细描述了如何通过神经网络和FA对问题进行建模以进行分析。一种用于NN的委员会学习方法已被用来提高泛化率。我们证明，尽管仅使用最基本的网络结构，我们的NN方法仍能够产生有希望的预测结果。更有趣的是，通过统计分析过程，已将具有相似生物学功能的基因聚类。另外，已经发现参与分解脂质的基因标记与CAD最相关。

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