Application and validation of machine-learning frameworks on big functional datasets to identify proteins important for cellular ageing.

The project applies state-of-the-art experimental and computational approaches to address fundamental questions relating to the genetics of ageing. The project aligns with the Pharmaceutical sector which may benefit by recruiting a highly skilled scientist trained by this project, and from the data generated to develop broad-spectrum, preventative measures that reduce the effects of ageing as the major risk factor for multiple diseases. Any pharmacological measures that promote healthy ageing would evidently be of massive benefit to our economy, quality of life, and health.

The project addresses the Grand Challenge 'AI & Data-Driven Economy' by increasing capacity and capability in strategically relevant areas of large-scale, genomic data sets and machine learning applications, through the provision of inter-disciplinary training in key skills for jobs of the future. These approaches have wide-ranging and increasing applications that reach beyond basic science. As such, the project will contribute to putting the UK at the forefront of the machine learning and data revolution.