计算生物学
表观基因组
系统生物学
生物
疾病
生物网络
转录组
基因组学
机制(生物学)
蛋白质组
基因调控网络
基因
神经科学
遗传学
生物信息学
基因组
DNA甲基化
基因表达
医学
哲学
病理
认识论
作者
Zuo-Teng Wang,Chen‐Chen Tan,Lan Tan,Jin‐Tai Yu
标识
DOI:10.1016/j.neubiorev.2018.11.007
摘要
Gene mining has been a fruitful approach in the study of Alzheimer's disease (AD). As a new starting point for studying AD, genetic and genomic investigations consistently strive to discover causative variants that are related to disease pathophysiology. Currently, genetic and genomic approaches have identified numerous loci. However, the elaboration of AD mechanism lagged behind gene discovery. The extensive use of parallel, high-throughput, next-generation sequencing techniques has improved our understanding of the roles of genetic variants in the brain at the highest level of functional hierarchy. We highlight three molecular systems (the transcriptome, proteome and epigenome) in this review to ascertain whether the methods used in systems biology studies of AD are useful. Here, we present many advantages of the high-throughput molecular, integrative and network methods, which may provide a good reference for future studies employing network biology approaches and large datasets.
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