TY - JOUR
T1 - Development of model based on clock gene expression of human hair follicle cells to estimate circadian time
AU - Lee, Taek
AU - Cho, Chul Hyun
AU - Kim, Woon Ryoung
AU - Moon, Joung Ho
AU - Kim, Soojin
AU - Geum, Dongho
AU - In, Hoh Peter
AU - Lee, Heon Jeong
N1 - Funding Information:
This study was supported by the Korea Health 21 R&D Project funded by the National Research Foundation of Korea (2017M3A9F1031220 and 2019R1A2C2084158)
PY - 2020
Y1 - 2020
N2 - Considering the effects of circadian misalignment on human pathophysiology and behavior, it is important to be able to detect an individual’s endogenous circadian time. We developed an endogenous Clock Estimation Model (eCEM) based on a machine learning process using the expression of 10 circadian genes. Hair follicle cells were collected from 18 healthy subjects at 08:00, 11:00, 15:00, 19:00, and 23:00 h for two consecutive days, and the expression patterns of 10 circadian genes were obtained. The eCEM was designed using the inverse form of the circadian gene rhythm function (i.e., Circadian Time = F(gene)), and the accuracy of eCEM was evaluated by leave-one-out cross-validation (LOOCV). As a result, six genes (PER1, PER3, CLOCK, CRY2, NPAS2, and NR1D2) were selected as the best model, and the error range between actual and predicted time was 3.24 h. The eCEM is simple and applicable in that a single time-point sampling of hair follicle cells at any time of the day is sufficient to estimate the endogenous circadian time.
AB - Considering the effects of circadian misalignment on human pathophysiology and behavior, it is important to be able to detect an individual’s endogenous circadian time. We developed an endogenous Clock Estimation Model (eCEM) based on a machine learning process using the expression of 10 circadian genes. Hair follicle cells were collected from 18 healthy subjects at 08:00, 11:00, 15:00, 19:00, and 23:00 h for two consecutive days, and the expression patterns of 10 circadian genes were obtained. The eCEM was designed using the inverse form of the circadian gene rhythm function (i.e., Circadian Time = F(gene)), and the accuracy of eCEM was evaluated by leave-one-out cross-validation (LOOCV). As a result, six genes (PER1, PER3, CLOCK, CRY2, NPAS2, and NR1D2) were selected as the best model, and the error range between actual and predicted time was 3.24 h. The eCEM is simple and applicable in that a single time-point sampling of hair follicle cells at any time of the day is sufficient to estimate the endogenous circadian time.
KW - Circadian clock
KW - circadian genes
KW - circadian time estimation
KW - hair follicle
KW - machine learning
UR - http://www.scopus.com/inward/record.url?scp=85087779827&partnerID=8YFLogxK
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U2 - 10.1080/07420528.2020.1777150
DO - 10.1080/07420528.2020.1777150
M3 - Article
C2 - 32654537
AN - SCOPUS:85087779827
SP - 1
EP - 9
JO - Annual Review of Chronopharmacology
JF - Annual Review of Chronopharmacology
SN - 0743-9539
ER -