Using machine learning to tell the time

该项目旨在采用机器学习的方法来准确预测植物的复杂性状。生物学家测量的许多表型和特征都是复杂的、耗时的，需要特定的技能。解决这一问题的一种有效方法是开发可靠的代理测量。该项目将首先开发一个使用转录数据预测内部生物时间的模型，以确定一个替代基因集，该基因集的表达可以准确地预测样本采集的生物时间。在过去的10年里，转录分析已经成为一种简单、可靠和相对便宜的分析方法。代用基因集将用于公共转录数据集，以调查基因和环境如何影响生物时间。这项工作将建立在今年发表的一篇论文的基础上(Gardiner等人。2021)，该模型应用机器学习来预测模式植物拟南芥中复杂的时间昼夜基因表达模式。该项目将继续开发其他重要性状的替代基因组，如作物产量、抗逆性、抗病性和氮素利用效率。

The project aims to take a machine learning approach to accurately predict complex traits in plants. Many of the phenotypes and traits that biologists measure are complex, time consuming and require a specific skill set. One powerful approach to getting around this is to develop robust proxy measurements. The project will initially develop a model to predict internal biological time using transcriptomic data, to identify a proxy gene set for which the expression can accurately predict the biological time the sample was taken. Over the last 10 years transcriptomic analysis has become a simple, robust, and relatively cheap assay. The proxy gene set will be used across public transcriptomic data sets to investigate how genotype and environment affect biological time. The work will build on a paper published this year (Gardiner et al. 2021), which applied machine learning to predict complex temporal circadian gene expression patterns in the model plant Arabidopsis thaliana. The project will go on to develop proxy gene sets for other important traits, such as crop yield, resilience, disease resistance and nitrogen use efficiency.