ZeitZeiger: supervised learning for high-dimensional data from an oscillatory system.
ZeitZeiger: supervised learning for high-dimensional data from an oscillatory system.
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DOI：
10.1093/nar/gkw030
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发表时间：
2016-05-05
影响因子：
14.9
通讯作者：
Butte AJ
中科院分区：
文献类型：
--
作者：
Hughey JJ;Hastie T;Butte AJ
许多生物系统随时间或空间振荡。尽管这些振荡器的重要性，从振荡系统的数据是有问题的现有方法的正则化监督学习。我们提出了ZeitZeiger，一种从高维观测预测周期变量（例如一天中的时间）的方法。ZeitZeiger学习与训练观测中的周期变量相关的变化的稀疏表示，然后使用最大似然法对测试观测进行预测。我们将ZeitZeiger应用于哺乳动物昼夜节律振荡器的全基因组基因表达的综合数据集。利用13个基因的表达，ZeitZeiger预测了12个小鼠器官中每个器官的昼夜节律时间（一天中的内部时间）在101小时内，从而产生了昼夜节律时间的多器官预测器。与最先进的方法相比，ZeitZeiger更快，更准确，使用的基因更少。然后，我们在包含近800个样本的20个额外数据集上验证了多器官预测因子。我们的研究结果表明，ZeitZeiger不仅做出了准确的预测，而且还提供了对数据来源的振荡器的行为和结构的洞察。随着我们从各种生物振荡器中收集高维数据的能力的提高，ZeitZeiger应该加强将这些数据转化为知识的努力。
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 datasets comprising nearly 800 samples. Our results suggest that ZeitZeiger not only makes accurate predictions, but also gives insight into the behavior and structure of the oscillator from which the data originated. As our ability to collect high-dimensional data from various biological oscillators increases, ZeitZeiger should enhance efforts to convert these data to knowledge.