ZeitZeiger: supervised learning for high-dimensional data from an oscillatory system

Numerous biological systems oscillate over time or space. Despite these oscillators’ importance, data from an oscillatory system is problematic for existing methods of regularized supervised learning. We present ZeitZeiger, a method to predict a periodic variable (e.g. time of day) from a high-dimensional observation. ZeitZeiger learns a sparse representation of the variation associated with the periodic variable in the training observations, then uses maximum-likelihood to make a prediction for a test observation. We applied ZeitZeiger to a comprehensive dataset of genome-wide gene expression from the mammalian circadian oscillator. Using the expression of 13 genes, ZeitZeiger predicted circadian time (internal time of day) in each of 12 mouse organs to within ∼1 h, resulting in a multi-organ predictor of circadian time. Compared to the state-of-the-art approach, ZeitZeiger was faster, more accurate and used fewer genes. We then validated the multi-organ predictor on 20 additional data...

诸多生物系统会随时间或空间呈现周期性振荡行为。尽管此类振荡系统具备重要研究价值，但现有正则化监督学习（regularized supervised learning）方法难以有效处理来自振荡系统的观测数据。我们提出ZeitZeiger：一种可从高维观测数据中预测周期性变量（如每日时刻）的方法。ZeitZeiger可先学习训练观测数据中与目标周期性变量相关的变异的稀疏表征，随后通过最大似然估计（maximum-likelihood）对测试观测数据完成预测。我们将ZeitZeiger应用于一套源自哺乳动物昼夜节律振荡器（circadian oscillator）的全基因组基因表达（genome-wide gene expression）综合数据集。仅依托13个基因的表达量，ZeitZeiger即可对小鼠12个器官的昼夜时间（circadian time，即内部日时刻）进行预测，预测误差约为1小时，由此构建出多器官昼夜时间预测器。与当前领域前沿方法相比，ZeitZeiger不仅运行速度更快、预测精度更高，所需使用的基因数量也更少。随后我们利用额外的20组数据集对该多器官预测器展开了验证……