PMID- 29272314
OWN - NLM
STAT- MEDLINE
DCOM- 20180116
LR  - 20181113
IS  - 1932-6203 (Electronic)
IS  - 1932-6203 (Linking)
VI  - 12
IP  - 12
DP  - 2017
TI  - The integration of weighted gene association networks based on information
      entropy.
PG  - e0190029
LID - 10.1371/journal.pone.0190029 [doi]
AB  - 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.
FAU - Yang, Fan
AU  - Yang F
AD  - Department of Mathematics, Army Logistics University of PLA, Chongqing, China.
FAU - Wu, Duzhi
AU  - Wu D
AD  - Rongzhi College of Chongqing Technology and Business, Chongqing, China.
FAU - Lin, Limei
AU  - Lin L
AD  - Department of Mathematics, Army Logistics University of PLA, Chongqing, China.
FAU - Yang, Jian
AU  - Yang J
AD  - School of Pharmacy, Second Military Medical University, Shanghai, China.
FAU - Yang, Tinghong
AU  - Yang T
AD  - Department of Mathematics, Army Logistics University of PLA, Chongqing, China.
FAU - Zhao, Jing
AU  - Zhao J
AUID- ORCID: 0000-0002-2439-2876
AD  - Institute of Interdisciplinary Complex Research, Shanghai University of
      Traditional Chinese Medicine, Shanghai, China.
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20171222
PL  - United States
TA  - PLoS One
JT  - PloS one
JID - 101285081
SB  - IM
MH  - *Gene Regulatory Networks
MH  - Genetic Predisposition to Disease
MH  - Humans
MH  - Information Services
MH  - *Models, Theoretical
MH  - Obesity/genetics
MH  - *Systems Biology
PMC - PMC5741255
EDAT- 2017/12/23 06:00
MHDA- 2018/01/18 06:00
CRDT- 2017/12/23 06:00
PHST- 2017/04/27 00:00 [received]
PHST- 2017/12/06 00:00 [accepted]
PHST- 2017/12/23 06:00 [entrez]
PHST- 2017/12/23 06:00 [pubmed]
PHST- 2018/01/18 06:00 [medline]
AID - 10.1371/journal.pone.0190029 [doi]
AID - PONE-D-17-16268 [pii]
PST - epublish
SO  - PLoS One. 2017 Dec 22;12(12):e0190029. doi: 10.1371/journal.pone.0190029.
      eCollection 2017.