PMID- 30967120
OWN - NLM
STAT- MEDLINE
DCOM- 20190828
LR  - 20200225
IS  - 1471-2164 (Electronic)
IS  - 1471-2164 (Linking)
VI  - 20
IP  - Suppl 2
DP  - 2019 Apr 4
TI  - MRCNN: a deep learning model for regression of genome-wide DNA methylation.
PG  - 192
LID - 10.1186/s12864-019-5488-5 [doi]
LID - 192
AB  - BACKGROUND: Determination of genome-wide DNA methylation is significant for both 
      basic research and drug development. As a key epigenetic modification, this 
      biochemical process can modulate gene expression to influence the cell 
      differentiation which can possibly lead to cancer. Due to the involuted 
      biochemical mechanism of DNA methylation, obtaining a precise prediction is a 
      considerably tough challenge. Existing approaches have yielded good predictions, 
      but the methods either need to combine plenty of features and prerequisites or 
      deal with only hypermethylation and hypomethylation. RESULTS: In this paper, we 
      propose a deep learning method for prediction of the genome-wide DNA methylation, 
      in which the Methylation Regression is implemented by Convolutional Neural 
      Networks (MRCNN). Through minimizing the continuous loss function, experiments 
      show that our model is convergent and more precise than the state-of-art method 
      (DeepCpG) according to results of the evaluation. MRCNN also achieves the 
      discovery of de novo motifs by analysis of features from the training process. 
      CONCLUSIONS: Genome-wide DNA methylation could be evaluated based on the 
      corresponding local DNA sequences of target CpG loci. With the autonomous 
      learning pattern of deep learning, MRCNN enables accurate predictions of 
      genome-wide DNA methylation status without predefined features and discovers some 
      de novo methylation-related motifs that match known motifs by extracting sequence 
      patterns.
FAU - Tian, Qi
AU  - Tian Q
AD  - School of Automation Engineering, University of Electronic Science and Technology 
      of China, Chengdu, Sichuan, China.
FAU - Zou, Jianxiao
AU  - Zou J
AD  - School of Automation Engineering, University of Electronic Science and Technology 
      of China, Chengdu, Sichuan, China.
FAU - Tang, Jianxiong
AU  - Tang J
AD  - School of Automation Engineering, University of Electronic Science and Technology 
      of China, Chengdu, Sichuan, China.
FAU - Fang, Yuan
AU  - Fang Y
AD  - School of Automation Engineering, University of Electronic Science and Technology 
      of China, Chengdu, Sichuan, China.
FAU - Yu, Zhongli
AU  - Yu Z
AD  - School of Automation Engineering, University of Electronic Science and Technology 
      of China, Chengdu, Sichuan, China.
FAU - Fan, Shicai
AU  - Fan S
AD  - School of Automation Engineering, University of Electronic Science and Technology 
      of China, Chengdu, Sichuan, China. shicaifan@uestc.edu.cn.
AD  - Center for Informational Biology, University of Electronic Science and Technology 
      of China, Chengdu, Sichuan, China. shicaifan@uestc.edu.cn.
LA  - eng
PT  - Journal Article
DEP - 20190404
PL  - England
TA  - BMC Genomics
JT  - BMC genomics
JID - 100965258
SB  - IM
MH  - Algorithms
MH  - *CpG Islands
MH  - *DNA Methylation
MH  - *Deep Learning
MH  - Epigenesis, Genetic
MH  - *Genome, Human
MH  - Genomics/*methods
MH  - Humans
MH  - *Models, Statistical
MH  - Regression Analysis
MH  - Sequence Analysis, DNA/*methods
PMC - PMC6457069
OTO - NOTNLM
OT  - Convolutional neuro networks
OT  - Genome-wide DNA methylation
OT  - Regression
COIS- ETHICS APPROVAL AND CONSENT TO PARTICIPATE: Not applicable. CONSENT FOR 
      PUBLICATION: Not applicable. COMPETING INTERESTS: The authors declare that they 
      have no competing interests. PUBLISHER'S NOTE: Springer Nature remains neutral 
      with regard to jurisdictional claims in published maps and institutional 
      affiliations.
EDAT- 2019/04/11 06:00
MHDA- 2019/08/29 06:00
PMCR- 2019/04/04
CRDT- 2019/04/11 06:00
PHST- 2019/04/11 06:00 [entrez]
PHST- 2019/04/11 06:00 [pubmed]
PHST- 2019/08/29 06:00 [medline]
PHST- 2019/04/04 00:00 [pmc-release]
AID - 10.1186/s12864-019-5488-5 [pii]
AID - 5488 [pii]
AID - 10.1186/s12864-019-5488-5 [doi]
PST - epublish
SO  - BMC Genomics. 2019 Apr 4;20(Suppl 2):192. doi: 10.1186/s12864-019-5488-5.