Recent trend on biological data at a molecular level is omics data analysis for both bulk and single cells, including genomics, proteomics, metabolomics, and epigenetics data (Wang and Zhang, 2017; Zhang et al., 2017; Zhao and Li, 2017; Cheng and Leung, 2018). Rapid accumulation of such high-dimensional biological data is driving the system-level study from describing complex phenomena to understanding molecular mechanisms (Park et al., 2018; Sun et al., 2018) and from analyzing individual components to understanding their networks and systems (Chen et al., 2009; Chen, 2017). Omics data analysis from the perspective of computational systems biology is increasingly attracting the attention from computational biology community, which aims to provide essential tools for gaining new insights into biological processes or systems (Zhang et al., 2015; Sa et al., 2016; Li et al., 2017; Liu et al., 2019a, b). In this issue, we collect six research articles and one Perspective, which are all related to such high-dimensional omics data analysis, ranged from new concepts of biomarkers (network biomarker for disease diagnosis and dynamic network biomarker (DNB) for disease prediction) to single-cell sequencing analyses, to neuron science and disease analyses. These papers were mainly from the contributors to The 12th International Conference on Computational Systems Biology (ISB 2018).

中文翻译：

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用于组学数据分析的计算系统生物学。

分子水平上生物数据的最新趋势是对大细胞和单细胞的组学数据分析，包括基因组学、蛋白质组学、代谢组学和表观遗传学数据（Wang和Zhang，2017；Zhang等人，2017；Zhao和Li，2017；程和梁，2018）。这种高维生物数据的快速积累正在推动系统级研究从描述复杂现象到理解分子机制（Park et al., 2018; Sun et al., 2018），从分析单个组件到理解其网络和系统（陈等人，2009；陈，2017）。从计算系统生物学角度进行组学数据分析越来越受到计算生物学界的关注，其目的是为获得对生物过程或系统的新见解提供必要的工具（Zhang et al., 2015；Sa et al., 2016； Li等人，2017；Liu等人，2019a，b)。本期我们收集了六篇研究文章和一篇观点，均与此类高维组学数据分析相关，范围从生物标志物的新概念（用于疾病诊断的网络生物标志物和用于疾病预测的动态网络生物标志物（DNB））到单细胞测序分析、神经元科学和疾病分析。这些论文主要来自第十二届计算系统生物学国际会议（ISB 2018）的投稿者。