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.

考虑到昼夜节律紊乱对人类病理生理和行为的影响，重要的是能够检测出个体的内源性昼夜节律时间。我们基于机器学习过程，使用10个昼夜节律基因的表达，开发了一个内源时钟估计模型（eCEM）。连续2天于08：00、11：00、15：00、19：00和23:00从18名健康受试者的毛囊细胞中收集毛囊细胞，获得10个昼夜节律基因的表达模式。使用昼夜节律基因节奏函数的逆形式（即，昼夜节律时间= F（基因））设计eCEM，并通过留一法交叉验证（LOOCV）评估eCEM的准确性。结果，六个基因（PER1，PER3，CLOCK，CRY2，NPAS2和NR1D2）选择最佳模型，实际时间与预测时间之间的误差范围为3.24小时。eCEM简单且适用，因为在一天的任何时间对毛囊细胞进行单个时间点采样就足以估算内源性昼夜节律时间。