Di Nanni Noemi, Bersanelli Matteo, Milanesi Luciano, Mosca Ettore
Institute of Biomedical Technologies, National Research Council, Milan, Italy.
Department of Industrial and Information Engineering, University of Pavia, Pavia, Italy.
Front Genet. 2020 Feb 27;11:106. doi: 10.3389/fgene.2020.00106. eCollection 2020.
The development of integrative methods is one of the main challenges in bioinformatics. Network-based methods for the analysis of multiple gene-centered datasets take into account known and/or inferred relations between genes. In the last decades, the mathematical machinery of network diffusion-also referred to as network propagation-has been exploited in several network-based pipelines, thanks to its ability of amplifying association between genes that lie in network proximity. Indeed, network diffusion provides a quantitative estimation of network proximity between genes associated with one or more different data types, from simple binary vectors to real vectors. Therefore, this powerful data transformation method has also been increasingly used in integrative analyses of multiple collections of biological scores and/or one or more interaction networks. We present an overview of the state of the art of bioinformatics pipelines that use network diffusion processes for the integrative analysis of omics data. We discuss the fundamental ways in which network diffusion is exploited, open issues and potential developments in the field. Current trends suggest that network diffusion is a tool of broad utility in omics data analysis. It is reasonable to think that it will continue to be used and further refined as new data types arise (e.g. single cell datasets) and the identification of system-level patterns will be considered more and more important in omics data analysis.
整合方法的发展是生物信息学的主要挑战之一。基于网络的多基因中心数据集分析方法考虑了基因之间已知和/或推断的关系。在过去几十年中，网络扩散的数学机制（也称为网络传播）已被应用于多个基于网络的流程中，这得益于其放大网络中相邻基因之间关联的能力。实际上，网络扩散提供了对与一种或多种不同数据类型相关的基因之间网络邻近性的定量估计，这些数据类型从简单的二元向量到实向量。因此，这种强大的数据转换方法也越来越多地用于生物分数的多个集合和/或一个或多个相互作用网络的整合分析。我们概述了使用网络扩散过程进行组学数据整合分析的生物信息学流程的现状。我们讨论了利用网络扩散的基本方式、该领域的开放问题和潜在发展。当前趋势表明，网络扩散是组学数据分析中具有广泛用途的工具。可以合理地认为，随着新数据类型（如单细胞数据集）的出现，它将继续被使用并进一步完善，并且在组学数据分析中，系统级模式的识别将被认为越来越重要。
