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首页> 外文会议>33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society >Phenotype prediction by integrative network analysis of SNP and gene expression microarrays
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Phenotype prediction by integrative network analysis of SNP and gene expression microarrays

机译：通过SNP和基因表达微阵列的综合网络分析预测表型

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A long-term goal of biomedical research is to decipher how genetic processes influence disease formation. Ubiquitous and advancing microarray technology can measure millions of DNA structural variants (single-nucleotide polymorphisms, or SNPs) and thousands of gene transcripts (RNA expression microarrays) in cells. Both of these information modalities can be brought to bear on disease etiology. This paper develops a Bayesian network-based approach to integrate SNP and expression microarray data. The network models SNP-gene interactions using a phenotype-centric network. Inferring the network consists of two steps: variable selection and network learning. The learned network illustrates how functionally dependent SNPs and genes influence each other, and also serves as a predictor of the phenotype. The application of the proposed method to a pediatric acute lymphoblastic leukemia dataset demonstrates the feasibility of our approach and its impact on biological investigation and clinical practice.
机译：生物医学研究的长期目标是破译遗传过程如何影响疾病的形成。无处不在且先进的微阵列技术可以测量细胞中数百万个DNA结构变异（单核苷酸多态性，即SNP）和成千上万个基因转录物（RNA表达微阵列）。这两种信息方式都可以影响疾病的病因。本文开发了一种基于贝叶斯网络的方法来整合SNP和表达微阵列数据。该网络使用以表型为中心的网络对SNP基因相互作用进行建模。推断网络包括两个步骤：变量选择和网络学习。习得的网络说明了功能依赖性SNP和基因如何相互影响，并且还充当了表型的预测因子。所提出的方法在儿科急性淋巴细胞白血病数据集上的应用证明了我们方法的可行性及其对生物学研究和临床实践的影响。

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