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Reverse engineering of gene regulatory networks from biological data
Author(s) -
Liu LiZhi,
Wu FangXiang,
Zhang WenJun
Publication year - 2012
Publication title -
wiley interdisciplinary reviews: data mining and knowledge discovery
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.506
H-Index - 47
eISSN - 1942-4795
pISSN - 1942-4787
DOI - 10.1002/widm.1068
Subject(s) - biological network , gene regulatory network , biological data , reverse engineering , computer science , inference , network topology , data mining , data science , machine learning , artificial intelligence , computational biology , biology , bioinformatics , gene , biochemistry , gene expression , programming language , operating system
Reverse engineering of gene regulatory networks (GRNs) is one of the most challenging tasks in systems biology and bioinformatics. It aims at revealing network topologies and regulation relationships between components from biological data. Owing to the development of biotechnologies, various types of biological data are collected from experiments. With the availability of these data, many methods have been developed to infer GRNs. This paper firstly provides an introduction to the basic biological background and the general idea of GRN inferences. Then, different methods are surveyed from two aspects: models that those methods are based on and inference algorithms that those methods use. The advantages and disadvantages of these models and algorithms are discussed. © 2012 Wiley Periodicals, Inc. This article is categorized under: Algorithmic Development > Biological Data Mining