PMID- 34295899
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20240402
IS  - 2296-634X (Print)
IS  - 2296-634X (Electronic)
IS  - 2296-634X (Linking)
VI  - 9
DP  - 2021
TI  - DeepGP: An Integrated Deep Learning Method for Endocrine Disease Gene Prediction 
      Using Omics Data.
PG  - 700061
LID - 10.3389/fcell.2021.700061 [doi]
LID - 700061
AB  - Endocrinology is the study focusing on hormones and their actions. Hormones are 
      known as chemical messengers, released into the blood, that exert functions 
      through receptors to make an influence in the target cell. The capacity of the 
      mammalian organism to perform as a whole unit is made possible based on two 
      principal control mechanisms, the nervous system and the endocrine system. The 
      endocrine system is essential in regulating growth and development, tissue 
      function, metabolism, and reproductive processes. Endocrine diseases such as 
      diabetes mellitus, Grave's disease, polycystic ovary syndrome, and insulin-like 
      growth factor I deficiency (IGFI deficiency) are classical endocrine diseases. 
      Endocrine dysfunction is also an increasing factor of morbidity in cancer and 
      other dangerous diseases in humans. Thus, it is essential to understand the 
      diseases from their genetic level in order to recognize more pathogenic genes and 
      make a great effort in understanding the pathologies of endocrine diseases. In 
      this study, we proposed a deep learning method named DeepGP based on graph 
      convolutional network and convolutional neural network for prioritizing 
      susceptible genes of five endocrine diseases. To test the performance of our 
      method, we performed 10-cross-validations on an integrated reported dataset; 
      DeepGP obtained a performance of the area under the curve of  approximately 83% and area under 
      the precision-recall curve of  approximately 65%. We found that type 1 diabetes mellitus (T1DM) 
      and type 2 diabetes mellitus (T2DM) share most of their associated genes; 
      therefore, we should pay more attention to the rest of the genes related to T1DM 
      and T2DM, respectively, which could help in understanding the pathogenesis and 
      pathologies of these diseases.
CI  - Copyright (c) 2021 Zhang, Wang, Xu, Zhang and Zang.
FAU - Zhang, Ningyi
AU  - Zhang N
AD  - School of Computer Science and Technology, Harbin Institute of Technology, 
      Harbin, China.
FAU - Wang, Haoyan
AU  - Wang H
AD  - School of Computer Science and Technology, Harbin Institute of Technology, 
      Harbin, China.
FAU - Xu, Chen
AU  - Xu C
AD  - Center for Bioinformatics, Harbin Institute of Technology, Harbin, China.
FAU - Zhang, Liyuan
AU  - Zhang L
AD  - School of Computer Science and Technology, Harbin Institute of Technology, 
      Harbin, China.
FAU - Zang, Tianyi
AU  - Zang T
AD  - School of Computer Science and Technology, Harbin Institute of Technology, 
      Harbin, China.
LA  - eng
PT  - Journal Article
DEP - 20210706
PL  - Switzerland
TA  - Front Cell Dev Biol
JT  - Frontiers in cell and developmental biology
JID - 101630250
PMC - PMC8290361
OTO - NOTNLM
OT  - Graves' disease
OT  - IGF-I
OT  - PCOS
OT  - T1DM
OT  - T2DM
OT  - deep learning methods
OT  - endocrine disease
COIS- The authors declare that the research was conducted in the absence of any 
      commercial or financial relationships that could be construed as a potential 
      conflict of interest.
EDAT- 2021/07/24 06:00
MHDA- 2021/07/24 06:01
PMCR- 2021/01/01
CRDT- 2021/07/23 06:52
PHST- 2021/04/25 00:00 [received]
PHST- 2021/05/31 00:00 [accepted]
PHST- 2021/07/23 06:52 [entrez]
PHST- 2021/07/24 06:00 [pubmed]
PHST- 2021/07/24 06:01 [medline]
PHST- 2021/01/01 00:00 [pmc-release]
AID - 10.3389/fcell.2021.700061 [doi]
PST - epublish
SO  - Front Cell Dev Biol. 2021 Jul 6;9:700061. doi: 10.3389/fcell.2021.700061. 
      eCollection 2021.