Using machine learning to tell the time

点击翻译按钮获取中文摘要

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.