College of Information Engineering, Shanghai Maritime University, 1550 Haigang Ave., Shanghai, 201306, People's Republic of China.
Department of Biochemistry, Rowan University and Guava Medicine, Glassboro, NJ, 08028, USA.
Interdiscip Sci. 2019 Jun;11(2):226-236. doi: 10.1007/s12539-018-0297-0. Epub 2018 Apr 19.
The focus of modern biomedical research concentrates on molecular level regulatory mechanisms and how the normal and abnormal phenotypes of tissue functional are affected by regulatory mechanisms. Most of the research on regulatory mechanism starts from the reconstruction of gene regulation network. At present, a large number of reconstruction methods construct the network using a single data set. These methods of inferring and predicting the relationship between the target gene and the transcription factor (TF) can be used to identify individual interactions between genes, while there is not much research on the interaction of many functional-related genes. In this paper, an integrated approach based on multi-data fusion is used to reconstruct the network on Alzheimer's disease (AD) which is the most common form of dementia. It not only considers the interaction between many functional-related genes and the TFs that have important implications for regulatory mechanisms, but also detects new genes associated with specific gene function expression. Protein interaction data, motif data and gene expression data of AD were integrated to gain insight into the underlying biological processes of AD. This method takes into account the TF on the target gene regulation, at the same time also considers co-expression mechanism of the TF and co-regulatory mechanism of the target gene. Eventually, not only a number of genes such as E2F4 and ATF1 related to the pathogenesis of AD have been identified, but also several significant biological processes, such as immunoregulation and neurogenesis, have been found to be associated with AD.
现代生物医学研究的重点集中在分子水平的调节机制上，以及组织功能的正常和异常表型如何受到调节机制的影响。大多数关于调节机制的研究都从基因调控网络的重建开始。目前，大量的重建方法使用单个数据集构建网络。这些推断和预测目标基因与转录因子（TF）之间关系的方法可用于识别基因之间的个体相互作用，而对于许多功能相关基因的相互作用研究却相对较少。在本文中，我们使用基于多数据融合的综合方法来重建最常见的痴呆症形式——阿尔茨海默病（AD）的网络。它不仅考虑了许多功能相关基因与 TF 之间的相互作用，这些 TF 对调节机制具有重要意义，而且还检测到了与特定基因功能表达相关的新基因。我们整合了 AD 的蛋白质相互作用数据、基序数据和基因表达数据，以深入了解 AD 的潜在生物学过程。该方法考虑了对靶基因调控的 TF，同时还考虑了 TF 的共表达机制和靶基因的共调节机制。最终，不仅确定了与 AD 发病机制相关的 E2F4 和 ATF1 等一些基因，还发现了一些与 AD 相关的重要生物学过程，如免疫调节和神经发生。
