Development of model based on clock gene expression of human hair follicle cells to estimate circadian time

Taek Lee, Chul Hyun Cho, Woon Ryoung Kim, Joung Ho Moon, Soojin Kim, Dongho Geum, Hoh Peter In, Heon Jeong Lee

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)993-1001
Number of pages9
JournalChronobiology International
Publication statusPublished - 2020

Bibliographical note

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)

Publisher Copyright:
© 2020, © 2020 Taylor & Francis Group, LLC.


  • Circadian clock
  • circadian genes
  • circadian time estimation
  • hair follicle
  • machine learning

ASJC Scopus subject areas

  • Physiology
  • Physiology (medical)


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