School of Computer Science and Engineering, Central South University, Human, China.
School of Computer Science and Engineering, Central South University, China.
Brief Bioinform. 2021 Sep 2;22(5). doi: 10.1093/bib/bbab080.
The prediction of genes related to diseases is important to the study of the diseases due to high cost and time consumption of biological experiments. Network propagation is a popular strategy for disease-gene prediction. However, existing methods focus on the stable solution of dynamics while ignoring the useful information hidden in the dynamical process, and it is still a challenge to make use of multiple types of physical/functional relationships between proteins/genes to effectively predict disease-related genes. Therefore, we proposed a framework of network impulsive dynamics on multiplex biological network (NIDM) to predict disease-related genes, along with four variants of NIDM models and four kinds of impulsive dynamical signatures (IDSs). NIDM is to identify disease-related genes by mining the dynamical responses of nodes to impulsive signals being exerted at specific nodes. By a series of experimental evaluations in various types of biological networks, we confirmed the advantage of multiplex network and the important roles of functional associations in disease-gene prediction, demonstrated superior performance of NIDM compared with four types of network-based algorithms and then gave the effective recommendations of NIDM models and IDS signatures. To facilitate the prioritization and analysis of (candidate) genes associated to specific diseases, we developed a user-friendly web server, which provides three kinds of filtering patterns for genes, network visualization, enrichment analysis and a wealth of external links (http://bioinformatics.csu.edu.cn/DGP/NID.jsp). NIDM is a protocol for disease-gene prediction integrating different types of biological networks, which may become a very useful computational tool for the study of disease-related genes.
疾病相关基因的预测对于疾病研究很重要，因为生物实验的成本和时间消耗都很高。网络传播是疾病基因预测的一种流行策略。然而，现有的方法侧重于动力学的稳定解，而忽略了隐藏在动力学过程中的有用信息，如何利用蛋白质/基因之间的多种物理/功能关系来有效地预测疾病相关基因仍然是一个挑战。因此，我们提出了一种基于多重生物网络的网络脉冲动力学框架（NIDM）来预测疾病相关基因，同时提出了 NIDM 模型的四个变体和四种脉冲动力学特征（IDS）。NIDM 通过挖掘节点对施加在特定节点上的脉冲信号的动力学响应来识别疾病相关基因。通过在各种类型的生物网络中进行的一系列实验评估，我们证实了多重网络的优势以及功能关联在疾病基因预测中的重要作用，证明了 NIDM 与四种基于网络的算法相比具有优越的性能，然后给出了 NIDM 模型和 IDS 特征的有效建议。为了便于对特定疾病相关的（候选）基因进行优先级排序和分析，我们开发了一个用户友好的网络服务器，它为基因提供了三种过滤模式、网络可视化、富集分析和丰富的外部链接（http://bioinformatics.csu.edu.cn/DGP/NID.jsp）。NIDM 是一种整合了不同类型生物网络的疾病基因预测协议，它可能成为研究疾病相关基因的一个非常有用的计算工具。
