Conserved Disease Modules Extracted From Multilayer Heterogeneous Disease and Gene Networks for Understanding Disease Mechanisms and Predicting Disease Treatments
Conserved Disease Modules Extracted From Multilayer Heterogeneous Disease and Gene Networks for Understanding Disease Mechanisms and Predicting Disease Treatments
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从多层异质疾病和基因网络中提取的保守疾病模块，用于了解疾病机制和预测疾病治疗
DOI：
10.3389/fgene.2018.00745
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发表时间：
2019-01
影响因子：
3.7
通讯作者：
Zha Yunhong
中科院分区：
文献类型：
--
作者：
Yu Liang;Yao Shunyu;Gao Lin;Zha Yunhong
疾病关系研究对于了解复杂疾病的发病机制、诊断、预后和药物开发具有重要意义。传统方法考虑一种类型的疾病数据或将多种类型的疾病数据聚合到单个网络中，这会导致与时间或上下文相关的重要信息丢失，并可能扭曲实际组织。因此，有必要应用多层网络模型来考虑疾病之间的多种类型的关系以及不同关系之间的重要相互作用。此外，从多层网络提取的模块更小，重叠更多，可以更好地反映实际组织。在这里，我们构建了一个加权的四层疾病-疾病相似网络来表征疾病之间不同水平的关联。然后，使用基于张量的计算框架从四层疾病网络中提取保守疾病模块(CDM)。过滤后，保留了9个有意义的CDM。经统计学意义检验，证明了9个疾病预防控制指标的显著性。与四个单层网络得到的模型相比，CMD更小，更能代表实际关系，包含潜在的疾病-疾病关系。KEGG途径、浓缩分析和文献挖掘进一步证实了这些疾病预防控制机制是高度可靠的。此外，CDMS还可以用于预测潜在的治疗疾病的药物。利用分子对接技术为药物治疗相关疾病提供直接证据。以类风湿性关节炎(RA)为例，我们发现了它的三种潜在药物卡维地洛、美托洛尔和雷米普利。许多研究指出卡维地洛和雷米普利对类风湿关节炎有疗效。总体而言，从多层网络中提取的CMD为我们从多层网络的角度理解疾病机制提供了令人印象深刻的信息，也为基于同一CDM中的邻居预测疾病的潜在药物提供了一种有效的方法。
Disease relationship studies for understanding the pathogenesis of complex diseases, diagnosis, prognosis, and drug development are important. Traditional approaches consider one type of disease data or aggregating multiple types of disease data into a single network, which results in important temporal- or context-related information loss and may distort the actual organization. Therefore, it is necessary to apply multilayer network model to consider multiple types of relationships between diseases and the important interplays between different relationships. Further, modules extracted from multilayer networks are smaller and have more overlap that better capture the actual organization. Here, we constructed a weighted four-layer disease-disease similarity network to characterize the associations at different levels between diseases. Then, a tensor-based computational framework was used to extract Conserved Disease Modules (CDMs) from the four-layer disease network. After filtering, nine significant CDMs were reserved. The statistical significance test proved the significance of the nine CDMs. Comparing with modules got from four single layer networks, CMDs are smaller, better represent the actual relationships, and contain potential disease-disease relationships. KEGG pathways enrichment analysis and literature mining further contributed to confirm that these CDMs are highly reliable. Furthermore, the CDMs can be applied to predict potential drugs for diseases. The molecular docking techniques were used to provide the direct evidence for drugs to treat related disease. Taking Rheumatoid Arthritis (RA) as a case, we found its three potential drugs Carvedilol, Metoprolol, and Ramipril. And many studies have pointed out that Carvedilol and Ramipril have an effect on RA. Overall, the CMDs extracted from multilayer networks provide us with an impressive understanding disease mechanisms from the perspective of multi-layer network and also provide an effective way to predict potential drugs for diseases based on its neighbors in a same CDM.
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DOI：
10.1590/s1135-57272008000400008
发表时间：
2008-07
期刊：
--
影响因子：
--
作者：
M. Porta
通讯作者：
M. Porta
影响因子：
11.8
作者：
Shirwadkar CG;Samant R;Sankhe M;Deshpande R;Yagi S;Schuster FL;Sriram R;Visvesvara GS
通讯作者：
Visvesvara GS
基于人类通路的疾病网络
DOI：
10.1109/tcbb.2017.2774802
发表时间：
2019
期刊：
IEEE/ACM Transactions on Computational Biology and Bioinformatics
影响因子：
--
作者：
Yu Liang;Gao Lin
通讯作者：
Gao Lin
影响因子：
12.3
作者：
Hart GT;Ramani AK;Marcotte EM
通讯作者：
Marcotte EM
影响因子：
14.9
作者：
NCBI Resource Coordinators
通讯作者：
NCBI Resource Coordinators