Bethany is a computational plant scientist, interested in understanding how plant behaviour and development is impacted by the environment.
She was awarded a Postdoctoral Fellowship from the National Biosciences Institute (NBI) and Alan Turing Institute (ATI), to train in machine learning and artificial intelligence approaches, and their application to life sciences.
Bethany is working with the BRAVO project, led by Professor Lars Østergaard and Dr Rachel Wells, to understand how transcriptome dynamics and phenotypic changes are linked in Brassicas using machine learning.
The BRAVO project has collected both phenotypic data, such as branch and pod patterning, and transcriptome data throughout development, over a wide range of different Brassica accessions.
The aim of Bethany’s project is to identify how these phenotypic traits are genetically regulated. The current bottleneck towards achieving this goal is data processing, as manually extracting quantitative data from images can be time-consuming and unreliable.
Recent advances in imaging and sensor data of plants has led to a deluge of information that remains largely unprocessed. Through machine learning, it is possible to extract patterns and features automatically, in a much shorter timeframe, opening up new research avenues.
Bethany hopes to develop an AI-based pipeline for the extraction of phenotypic data from images so that they can be used, in combination with the transcriptomic data, to explore the underlying genetic regulation during plant development.
By exploiting advanced machine learning approaches, she hopes to investigate plant development in response to environmental conditions, with a high-level of automation.