Using machine learning to tell the time

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.