Award to Bring Machine Learning into Life Sciences

Dr Gabriele Schweikert, a researcher at the University of Dundee’s School of Life Sciences has received a £1.6 million Future Leaders Fellowship award from UK Research and Innovation (UKRI) to develop machine learning tools for better understanding of molecular processes in living cells and how these go wrong in diseases.  

Working with Professor Tom Owen-Hughes, Head of the Gene Regulation and Expression Unit, on development of specific data analysis tools to better understand epigenetic regulation of gene expression, she will build a team in the Division of Computational Biology with the required interdisciplinary competence to maximise the potential of machine learning in scientific research.

“Currently few people are equipped to tackle the challenges that arise in the interface between advanced computational biology and wet-lab biology,” said Dr Schweikert.

“The rapid progress in machine learning technology as well as in experimental high throughput measurements, make it possible to find hidden patterns in large, complex data sets. These patterns can then be used to make predictions in similar circumstances that have not yet been observed.

“However, many questions that motivate researchers in the biomedical sciences are about the underlying causes of the observations. While algorithmic tools are available to tackle these questions as well, they are not widely used in practice. In most instances, causes cannot be computed from data alone, as they require additional knowledge of the data-generating process.”

Dr Schweikert initially trained as a physicist, but became interested in the biological sciences early in her undergraduate studies. During an interdisciplinary PhD at the Max Planck campus in Germany, she developed new computational systems for DNA sequence analysis. She started her own research group on computational epigenomics at Dundee last year and also holds an affiliation with the Cyber Valley Initiative, Germany, one of prime Europe’s centres for machine learning with a particular strength in causal inference.