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

该项目应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。该项目与制药部门保持一致，该部门可能受益于招募由该项目培训的高技能科学家，并从所产生的数据中制定广泛的预防措施，以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理学措施显然对我们的经济、生活质量和健康都有巨大的好处。该项目通过为未来工作提供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型经济”的重大挑战。这些方法在基础科学之外有着广泛和日益增长的应用。因此，该项目将有助于将英国置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.