Having previously worked with machine learning in computational biology during my undergrad and master’s in Birmingham, this project excited me as it is applying these techniques to a new and timely problem. The chance to learn cutting edge molecular biology techniques and apply them to have control over the growth of the dataset also drew me to the project.
Aggregation is a major hurdle to their manufacture resulting in the failure of promising candidate biologics even at very late stages in the development pipeline. The ability to identify sequences likely to aggregate during production, transport or storage is of crucial importance. We will be using machine learning (ML) to hopefully identify aggregation from highly complex datasets.