Prediction of lung function in adolescence using epigenetic aging: a machine learning approach

Simple item page
dc.contributor.authorMd, Adnan Arefeen
dc.contributor.authorNimi, Sumaiya Tabassum
dc.contributor.authorRahman, M. Sohel
dc.contributor.authorArshad, Syed Hasan
dc.contributor.authorHolloway, John W.
dc.contributor.authorRezwan, Faisal I.
dc.date.accessioned2021-02-15T14:41:33Z
dc.date.available2021-02-15T14:41:33Z
dc.date.issued2020-11-09
dc.description.abstractEpigenetic aging has been found to be associated with a number of phenotypes and diseases. A few studies have investigated its effect on lung function in relatively older people. However, this effect has not been explored in the younger population. This study examines whether lung function in adolescence can be predicted with epigenetic age accelerations (AAs) using machine learning techniques. DNA methylation based AAs were estimated in 326 matched samples at two time points (at 10 years and 18 years) from the Isle of Wight Birth Cohort. Five machine learning regression models (linear, lasso, ridge, elastic net, and Bayesian ridge) were used to predict FEV1 (forced expiratory volume in one second) and FVC (forced vital capacity) at 18 years from feature selected predictor variables (based on mutual information) and AA changes between the two time points. The best models were ridge regression (R2 = 75.21% ± 7.42%; RMSE = 0.3768 ± 0.0653) and elastic net regression (R2 = 75.38% ± 6.98%; RMSE = 0.445 ± 0.069) for FEV1 and FVC, respectively. This study suggests that the application of machine learning in conjunction with tracking changes in AA over the life span can be beneficial to assess the lung health in adolescenceen_UK
dc.identifier.citationArefeen MA, Nimi ST, Rahman MS, et al., (2020) Prediction of lung function in adolescence using epigenetic aging: a machine learning approach. Methods and Protocols, Volume 3, Issue 4, 2020, Article number 77en_UK
dc.identifier.issn2409-9279
dc.identifier.urihttps://doi.org/10.3390/mps3040077
dc.identifier.urihttp://dspace.lib.cranfield.ac.uk/handle/1826/16343
dc.language.isoenen_UK
dc.publisherMDPIen_UK
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectmachine learningen_UK
dc.subjectlung functionen_UK
dc.subjecthyperparameter tuningen_UK
dc.subjectfeature selectionen_UK
dc.subjectepigenetic agingen_UK
dc.titlePrediction of lung function in adolescence using epigenetic aging: a machine learning approachen_UK
dc.typeArticleen_UK

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Prediction_of_lung_function_in_adolescence_using_epigenetic_aging-2020.pdf
Size:
619.28 KB
Format:
Adobe Portable Document Format
Description:
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.63 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections

Staff publications (SWEE)