PMID- 31263873
OWN - NLM
STAT- MEDLINE
DCOM- 20200828
LR  - 20240329
IS  - 1367-4811 (Electronic)
IS  - 1367-4803 (Print)
IS  - 1367-4803 (Linking)
VI  - 36
IP  - 1
DP  - 2020 Jan 1
TI  - Inference of differential gene regulatory networks based on gene expression and 
      genetic perturbation data.
PG  - 197-204
LID - 10.1093/bioinformatics/btz529 [doi]
AB  - MOTIVATION: Gene regulatory networks (GRNs) of the same organism can be different 
      under different conditions, although the overall network structure may be 
      similar. Understanding the difference in GRNs under different conditions is 
      important to understand condition-specific gene regulation. When gene expression 
      and other relevant data under two different conditions are available, they can be 
      used by an existing network inference algorithm to estimate two GRNs separately, 
      and then to identify the difference between the two GRNs. However, such an 
      approach does not exploit the similarity in two GRNs, and may sacrifice inference 
      accuracy. RESULTS: In this paper, we model GRNs with the structural equation 
      model (SEM) that can integrate gene expression and genetic perturbation data, and 
      develop an algorithm named fused sparse SEM (FSSEM), to jointly infer GRNs under 
      two conditions, and then to identify difference of the two GRNs. Computer 
      simulations demonstrate that the FSSEM algorithm outperforms the approaches that 
      estimate two GRNs separately. Analysis of a dataset of lung cancer and another 
      dataset of gastric cancer with FSSEM inferred differential GRNs in cancer versus 
      normal tissues, whose genes with largest network degrees have been reported to be 
      implicated in tumorigenesis. The FSSEM algorithm provides a valuable tool for 
      joint inference of two GRNs and identification of the differential GRN under two 
      conditions. AVAILABILITY AND IMPLEMENTATION: The R package fssemR implementing 
      the FSSEM algorithm is available at https://github.com/Ivis4ml/fssemR.git. It is 
      also available on CRAN. SUPPLEMENTARY INFORMATION: Supplementary data are 
      available at Bioinformatics online.
CI  - (c) The Author(s) 2019. Published by Oxford University Press. All rights reserved. 
      For permissions, please e-mail: journals.permissions@oup.com.
FAU - Zhou, Xin
AU  - Zhou X
AD  - Department of Electrical and Computer Engineering, University of Miami, FL 33146, 
      USA.
FAU - Cai, Xiaodong
AU  - Cai X
AD  - Department of Electrical and Computer Engineering, University of Miami, FL 33146, 
      USA.
LA  - eng
GR  - R01 GM104975/GM/NIGMS NIH HHS/United States
PT  - Journal Article
PT  - Research Support, N.I.H., Extramural
PT  - Research Support, U.S. Gov't, Non-P.H.S.
PL  - England
TA  - Bioinformatics
JT  - Bioinformatics (Oxford, England)
JID - 9808944
SB  - IM
MH  - *Algorithms
MH  - Computer Simulation
MH  - *Gene Expression Regulation
MH  - *Gene Regulatory Networks
MH  - Humans
MH  - *Models, Genetic
MH  - Neoplasms/genetics/physiopathology
PMC - PMC6956787
EDAT- 2019/07/03 06:00
MHDA- 2020/08/29 06:00
PMCR- 2021/01/01
CRDT- 2019/07/03 06:00
PHST- 2018/09/03 00:00 [received]
PHST- 2019/06/09 00:00 [revised]
PHST- 2019/06/28 00:00 [accepted]
PHST- 2019/07/03 06:00 [pubmed]
PHST- 2020/08/29 06:00 [medline]
PHST- 2019/07/03 06:00 [entrez]
PHST- 2021/01/01 00:00 [pmc-release]
AID - 5526871 [pii]
AID - btz529 [pii]
AID - 10.1093/bioinformatics/btz529 [doi]
PST - ppublish
SO  - Bioinformatics. 2020 Jan 1;36(1):197-204. doi: 10.1093/bioinformatics/btz529.