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).