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

该项目采用最先进的实验和计算方法来解决与衰老遗传学有关的基本问题。该项目与制药行业保持一致，制药行业可能会通过招募一名经过该项目培训的高技能科学家，并从生成的数据中受益，以制定广谱预防措施，减少衰老作为多种疾病的主要风险因素的影响。任何促进健康老龄化的药物措施都将对我们的经济、生活质量和健康产生巨大的益处。该项目通过提供未来工作关键技能的跨学科培训，提高大规模基因组数据集和机器学习应用等战略相关领域的能力，以应对“人工智能和数据驱动经济”的重大挑战。这些方法有着广泛和日益增长的应用，超越了基础科学。因此，该项目将有助于使英国处于机器学习和数据革命的最前沿。

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.