title: A Survey of Statistical Models for Reverse Engineering Gene Regulatory Networks.
abstract: Statistical models for reverse engineering gene regulatory networks are surveyed in this article. To provide readers with a system-level view of the modeling issues in this research, a graphical modeling framework is proposed. This framework serves as the scaffolding on which the review of different models can be systematically assembled. Based on the framework, we review many existing models for many aspects of gene regulation; the pros and cons of each model are discussed. In addition, network inference algorithms are also surveyed under the graphical modeling framework by the categories of point solutions and probabilistic solutions and the connections and differences among the algorithms are provided. This survey has the potential to elucidate the development and future of reverse engineering GRNs and bring statistical signal processing closer to the core of this research.
authors.affiliation: Department of Electrical and Computer Engineering, University of Texas at San Antonio, San Antonio, TX 78249-0669, yufei.huang@utsa.edu.
authors.forename: Yufei
authors.initials: Y
authors.lastname: Huang
authors.forename: Isabel M
authors.initials: IM
authors.lastname: Tienda-Luna
authors.forename: Yufeng
authors.initials: Y
authors.lastname: Wang