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February 2021 Network Modeling in Biology: Statistical Methods for Gene and Brain Networks
Y. X. Rachel Wang, Lexin Li, Jingyi Jessica Li, Haiyan Huang
Statist. Sci. 36(1): 89-108 (February 2021). DOI: 10.1214/20-STS792


The rise of network data in many different domains has offered researchers new insights into the problem of modeling complex systems and propelled the development of numerous innovative statistical methodologies and computational tools. In this paper, we primarily focus on two types of biological networks, gene networks and brain networks, where statistical network modeling has found both fruitful and challenging applications. Unlike other network examples such as social networks where network edges can be directly observed, both gene and brain networks require careful estimation of edges using measured data as a first step. We provide a discussion on existing statistical and computational methods for edge estimation and subsequent statistical inference problems in these two types of biological networks.


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Y. X. Rachel Wang. Lexin Li. Jingyi Jessica Li. Haiyan Huang. "Network Modeling in Biology: Statistical Methods for Gene and Brain Networks." Statist. Sci. 36 (1) 89 - 108, February 2021.


Published: February 2021
First available in Project Euclid: 21 December 2020

MathSciNet: MR4194205
Digital Object Identifier: 10.1214/20-STS792

Keywords: brain connectivity networks , Gene regulatory networks , Network inference , network reconstruction

Rights: Copyright © 2021 Institute of Mathematical Statistics


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Vol.36 • No. 1 • February 2021
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