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EpiSmokEr: a robust classifier to determine smoking status from DNA methylation data

Epigenomics, 2019-10, Vol.11 (13), p.1469-1486 [Peer Reviewed Journal]

2019 Future Medicine Ltd ;Copyright Future Medicine Ltd Oct 2019 ;ISSN: 1750-1911 ;EISSN: 1750-192X ;DOI: 10.2217/epi-2019-0206 ;PMID: 31466478

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Title:
EpiSmokEr: a robust classifier to determine smoking status from DNA methylation data
Author: Bollepalli, Sailalitha ; Korhonen, Tellervo ; Kaprio, Jaakko ; Anders, Simon ; Ollikainen, Miina
Subjects: Adult ; Aged ; Artificial intelligence ; Biomarkers ; Classifiers ; Computational Biology - methods ; CpG Islands ; Datasets ; Deoxyribonucleic acid ; DNA ; DNA Methylation ; Epigenesis, Genetic ; epigenetic smoking status ; Epigenetics ; Epigenomics - methods ; Female ; Gene expression ; Generalized linear models ; Genomes ; Humans ; Learning algorithms ; Machine Learning ; Male ; Middle Aged ; multinomial LASSO ; Probability ; Smoking ; smoking status classifier ; Software ; Studies ; tobacco smoking ; Tobacco Smoking - genetics
Is Part Of: Epigenomics, 2019-10, Vol.11 (13), p.1469-1486
Description: Smoking strongly influences DNA methylation, with current and never smokers exhibiting different methylation profiles. To advance the practical applicability of the smoking-associated methylation signals, we used machine learning methodology to train a classifier for smoking status prediction. We show the prediction performance of our classifier on three independent whole-blood datasets demonstrating its robustness and global applicability. Furthermore, we examine the reasons for biologically meaningful misclassifications through comprehensive phenotypic evaluation. The major contribution of our classifier is its global applicability without a need for users to determine a threshold value for each dataset to predict the smoking status. We provide an R package, EpiSmokEr (Epigenetic Smoking status Estimator), facilitating the use of our classifier to predict smoking status in future studies.
Publisher: England: Future Medicine Ltd
Language: English
Identifier: ISSN: 1750-1911
EISSN: 1750-192X
DOI: 10.2217/epi-2019-0206
PMID: 31466478
Source: MEDLINE
Alma/SFX Local Collection
ProQuest Central

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EpiSmokEr: a robust classifier to determine smoking status from DNA methylation data

2019 Future Medicine Ltd ;Copyright Future Medicine Ltd Oct 2019 ;ISSN: 1750-1911 ;EISSN: 1750-192X ;DOI: 10.2217/epi-2019-0206 ;PMID: 31466478

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