PMID- 28524769
OWN - NLM
STAT- MEDLINE
DCOM- 20171207
LR  - 20200306
IS  - 1559-2308 (Electronic)
IS  - 1559-2294 (Print)
IS  - 1559-2294 (Linking)
VI  - 12
IP  - 7
DP  - 2017 Jul 3
TI  - Machine learning for epigenetics and future medical applications.
PG  - 505-514
LID - 10.1080/15592294.2017.1329068 [doi]
AB  - Understanding epigenetic processes holds immense promise for medical 
      applications. Advances in Machine Learning (ML) are critical to realize this 
      promise. Previous studies used epigenetic data sets associated with the germline 
      transmission of epigenetic transgenerational inheritance of disease and novel ML 
      approaches to predict genome-wide locations of critical epimutations. A 
      combination of Active Learning (ACL) and Imbalanced Class Learning (ICL) was used 
      to address past problems with ML to develop a more efficient feature selection 
      process and address the imbalance problem in all genomic data sets. The power of 
      this novel ML approach and our ability to predict epigenetic phenomena and 
      associated disease is suggested. The current approach requires extensive 
      computation of features over the genome. A promising new approach is to introduce 
      Deep Learning (DL) for the generation and simultaneous computation of novel 
      genomic features tuned to the classification task. This approach can be used with 
      any genomic or biological data set applied to medicine. The application of 
      molecular epigenetic data in advanced machine learning analysis to medicine is 
      the focus of this review.
FAU - Holder, Lawrence B
AU  - Holder LB
AD  - a School of Electrical Engineering and Computer Science , Washington State 
      University , Pullman , WA , USA.
FAU - Haque, M Muksitul
AU  - Haque MM
AD  - a School of Electrical Engineering and Computer Science , Washington State 
      University , Pullman , WA , USA.
AD  - b Center for Reproductive Biology, School of Biological Sciences , Washington 
      State University , Pullman , WA , USA.
FAU - Skinner, Michael K
AU  - Skinner MK
AD  - b Center for Reproductive Biology, School of Biological Sciences , Washington 
      State University , Pullman , WA , USA.
LA  - eng
GR  - R01 ES012974/ES/NIEHS NIH HHS/United States
GR  - ES012974-10/ES/NIEHS NIH HHS/United States
PT  - Journal Article
PT  - Research Support, N.I.H., Extramural
PT  - Research Support, Non-U.S. Gov't
PT  - Research Support, U.S. Gov't, Non-P.H.S.
PT  - Review
DEP - 20170519
PL  - United States
TA  - Epigenetics
JT  - Epigenetics
JID - 101265293
SB  - IM
MH  - Animals
MH  - *Epigenesis, Genetic
MH  - Epigenomics/*methods
MH  - Genetics, Medical/*methods
MH  - Humans
MH  - *Machine Learning
PMC - PMC5687335
OTO - NOTNLM
OT  - Active learning
OT  - DNA methylation
OT  - deep learning
OT  - epigenetics
OT  - epigenome
OT  - imbalanced-class learning
OT  - machine learning
OT  - molecular diagnostics
EDAT- 2017/05/20 06:00
MHDA- 2017/12/08 06:00
PMCR- 2017/05/19
CRDT- 2017/05/20 06:00
PHST- 2017/05/20 06:00 [pubmed]
PHST- 2017/12/08 06:00 [medline]
PHST- 2017/05/20 06:00 [entrez]
PHST- 2017/05/19 00:00 [pmc-release]
AID - 1329068 [pii]
AID - 10.1080/15592294.2017.1329068 [doi]
PST - ppublish
SO  - Epigenetics. 2017 Jul 3;12(7):505-514. doi: 10.1080/15592294.2017.1329068. Epub 
      2017 May 19.