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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.<\/jats:p>","DOI":"10.1093\/bib\/bbaf223","type":"journal-article","created":{"date-parts":[[2025,5,22]],"date-time":"2025-05-22T07:37:39Z","timestamp":1747899459000},"source":"Crossref","is-referenced-by-count":5,"title":["Network analysis of multivariate time series data in biological systems: methods and applications"],"prefix":"10.1093","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1083-9542","authenticated-orcid":false,"given":"Hao","family":"Mei","sequence":"first","affiliation":[{"name":"Center for Applied Statistics , School of Statistics, Institute of Health Data Science, Renmin University of China, 59 Zhongguancun Street, 100872 Beijing,","place":["China"]}]},{"given":"Zhiyuan","family":"Wang","sequence":"additional","affiliation":[{"name":"Center for Applied Statistics , School of Statistics, 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