Open AccessThe integration of weighted gene association networks based on information entropy
Author(s) -
Fan Yang,
Duzhi Wu,
Limei Lin,
Jian Yang,
Tinghong Yang,
Jing Zhao
Publication year - 2017
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0190029
Subject(s) - gene regulatory network , systems biology , computer science , computational biology , data mining , biological network , entropy (arrow of time) , data integration , relevance (law) , gene , genome wide association study , biology , genetics , genotype , single nucleotide polymorphism , gene expression , physics , quantum mechanics , law , political science
Constructing genome scale weighted gene association networks (WGAN) from multiple data sources is one of research hot spots in systems biology. In this paper, we employ information entropy to describe the uncertain degree of gene-gene links and propose a strategy for data integration of weighted networks. We use this method to integrate four existing human weighted gene association networks and construct a much larger WGAN, which includes richer biology information while still keeps high functional relevance between linked gene pairs. The new WGAN shows satisfactory performance in disease gene prediction, which suggests the reliability of our integration strategy. Compared with existing integration methods, our method takes the advantage of the inherent characteristics of the component networks and pays less attention to the biology background of the data. It can make full use of existing biological networks with low computational effort.