单核苷酸多态性
计算生物学
疾病
SNP公司
精神分裂症(面向对象编程)
复杂疾病
DNA甲基化
生物
计算机科学
生物信息学
基因
遗传学
医学
基因型
病理
基因表达
程序设计语言
作者
Su-Ping Deng,Wenxing Hu,Vince D. Calhoun,Yu‐Ping Wang
标识
DOI:10.1109/tcbb.2017.2748944
摘要
It's increasingly important but difficult to determine potential biomarkers of schizophrenia (SCZ) disease, owing to the complex pathophysiology of this disease. In this study, a network-fusion based framework was proposed to identify genetic biomarkers of the SCZ disease. A three-step feature selection was applied to single nucleotide polymorphisms (SNPs), DNA methylation, and functional magnetic resonance imaging (fMRI) data to select important features, which were then used to construct two gene networks in different states for the SNPs and DNA methylation data, respectively. Two health networks (one is for SNP data and the other is for DNA methylation data) were combined into one health network from which health minimum spanning trees (MSTs) were extracted. Two disease networks also followed the same procedures. Those genes with significant changes were determined as SCZ biomarkers by comparing MSTs in two different states and they were finally validated from five aspects. The effectiveness of the proposed discovery framework was also demonstrated by comparing with other network-based discovery methods. In summary, our approach provides a general framework for discovering gene biomarkers of the complex diseases by integrating imaging genomic data, which can be applied to the diagnosis of the complex diseases in the future.
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