Department of Convergence Security Engineering, Sungshin University , Seoul, Republic of Korea.
Department of Psychiatry, Chungnam National University , Daejeon, Republic of Korea.
Chronobiol Int. 2020 Jul;37(7):993-1001. doi: 10.1080/07420528.2020.1777150. Epub 2020 Jul 13.
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 (, and 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.
考虑到昼夜节律失调对人体病理生理学和行为的影响，能够检测个体的内源性昼夜节律时间非常重要。我们开发了一种基于机器学习过程的内源性时钟估计模型 (eCEM)，该模型使用 10 个生物钟基因的表达。连续两天在 08:00、11:00、15:00、19:00 和 23:00 采集 18 名健康受试者的毛囊细胞，获得 10 个生物钟基因的表达模式。eCEM 是使用生物钟基因节律函数的逆形式 (即昼夜时间 = F(基因)) 设计的，通过留一法交叉验证 (LOOCV) 评估 eCEM 的准确性。结果表明，选择了六个基因 (, 和 作为最佳模型，实际时间和预测时间之间的误差范围为 3.24 小时。eCEM 简单易用，因为只需在一天中的任何时间采集单个毛囊细胞时间点即可估计内源性昼夜时间。
