Network analysis has become an essential tool in biological and biomedical research, providing insights into complex biological mechanisms. Since biological systems are inherently time-dependent, incorporating time-varying methods is crucial for capturing temporal changes, adaptive interactions, and evolving dependencies within networks. Our study explores key time-varying methodologies for network structure estimation and network inference based on observed structures. We begin by discussing approaches for estimating network structures from data, focusing on the time-varying Gaussian graphical model, dynamic Bayesian network, and vector autoregression-based causal analysis. Next, we examine analytical techniques that leverage pre-specified or observed networks, including other autoregression-based methods and latent variable models. Furthermore, we explore practical applications and computational tools designed for these methods. By synthesizing these approaches, our study provides a comprehensive evaluation of their strengths and limitations in the context of biological data analysis.

Abdominal aortic aneurysm (AAA) is a localized enlargement of the abdominal aorta. Once ruptured AAA (rAAA) happens, repairing procedures need to be applied immediately, for which there are two main options: open aortic repair (OAR) and endovascular aortic repair (EVAR). It is of great clinical significance to objectively compare the survival outcomes of OAR versus EVAR using randomized clinical trials; however, this has serious feasibility issues. In this study, with the Medicare data, we conduct an emulation analysis and explicitly “assemble” a clinical trial with rigorously defined inclusion/exclusion criteria. A total of 7826 patients are “recruited”, with 3866 and 3960 in the OAR and EVAR arms, respectively. Mimicking but significantly advancing from the regression-based literature, we adopt a deep learning-based analysis strategy, which consists of a propensity score step, a weighted survival analysis step, and a bootstrap step. The key finding is that for both short- and long-term mortality, EVAR has survival advantages. This study delivers a new big data strategy for addressing critical clinical problems and provides valuable insights into treating rAAA using OAR and EVAR.

Human disease network (HDN) analysis, which jointly considers a large number of diseases and focuses on their interconnections, is getting increasingly popular and can shed important insight not possessed by individual-disease-based analysis. Multiple network analysis techniques have been developed for HDNs, although new developments are still strongly needed. In this article we adopt latent space modeling, which has proven powerful in other network analysis contexts and offers unique, insightful interpretations, but has been limitedly applied in HDN analysis. Different from some other types of network analysis and some other HDN analyses (such as gene-centric ones), in this article we pay unique attention to modeling temporal variations. For this purpose, a penalization approach is developed, which can identify time regions with constant network structures (that correspond to ignorable changes) as well as those with smooth variations. The statistical and computational properties are rigorously established. With Medicare data—one of the most powerful medical claims databases—we analyze the admission records of 133 million hospital inpatient treatments from January 2008 to December 2019. Sensible findings are made on disease interconnections and clustering structures. Additionally, the temporal variations, which have not been revealed in the literature, are found to be interpretable. The analysis can provide a new way for connecting and grouping diseases and assist in understanding and planning medical resources.