Transfer Learning for Genome Analysis and Personalised Recommendation

用于基因组分析和个性化推荐的迁移学习

• 批准号: LP210100414
• 负责人: Prof Jie Lu
• 金额: $ 49.34万
• 依托单位: University of Technology Sydney
• 依托单位国家: 澳大利亚
• 项目类别: Linkage Projects
• 财政年份: 2021
• 资助国家: 澳大利亚
• 起止时间: 2021-07-01 至 2024-06-30
• 项目状态: 已结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/LP210100414
• 关键词: Transfer; Learning; Genome; Analysis; Personalised

This project aims to improve the accuracy, adaptability, and comprehensiveness of health characteristic predictions and provide personalised recommendations for healthcare service and disease prevention. The deliverables include uncertainty learning and multi-source transfer learning methodologies for predictions based on genome analysis that distils and transfers useful knowledge from multiple sources into an Australian genome analysis model. A federated cross-domain recommender system will be developed to profile individuals and generate personalised recommendations. The outcomes are expected to create a paradigm shift in learning-based prediction and personalised recommendations to support healthcare services in complex environments.

该项目旨在提高健康特征预测的准确性、适应性和全面性，并为医疗服务和疾病预防提供个性化建议。可交付成果包括基于基因组分析的预测的不确定性学习和多源转移学习方法，该方法将来自多个来源的有用知识提炼并转移到澳大利亚基因组分析模型中。将开发一个联合跨域推荐系统，以分析个人并生成个性化推荐。预计这些成果将在基于学习的预测和个性化建议方面产生范式转变，以支持复杂环境中的医疗服务。