Machine Learning of Genetic, Clinical and Environmental Data for Early Morbidity Detection in the UK Biobank.

英国生物库中用于早期发病检测的遗传、临床和环境数据的机器学习。

• 批准号: 2556925
• 金额: --
• 依托单位: King's College London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2018
• 资助国家: 英国
• 起止时间: 2018 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2556925
• 关键词: Machine; Learning; Genetic; Clinical; Environmental

Niels Bohr stated that 'It is Difficult to Make Predictions, Especially About the Future' but the development of data science methods allows us to build increasingly effective predictive models in large data sets. This PhD project will apply machine learning and deep learning methods, as well as classic statistical models, to the UK Biobank, an incredible health study of over 500,000 people in the UK. The student will integrate genetic, environmental and clinical data to predict onset of diseases that are relevant for the UK's aging population such as heart disease and cancer. A particular focus will be assessing the utility of genetic information: does genetics add information to routinely-collected clinical and biomarker data, and what role could genetics play in clinical prediction algorithms? In Year 1, the student will develop their programming, analytical and 'big data' skills, building classic statistical models and machine learning algorithms to assess the predictive ability of clinical data (including biometrics and blood biomarkers), lifestyle data (such as smoking habits, diet and exercise) and genetic predisposition in coronary artery disease. In Year 2, novel genetic risk scores will be built for different disorders, using machine learning methods, and their predictive ability assessed, in combination with all other sources of information. In addition, machine/deep learning methods will be used to identify new environmental risk factors. In Year 3, the student will build comprehensive disease risk models and test their predictive power against the gold-standard clinical prediction tools.

尼尔斯·鲍尔（Niels Bohr）表示，“很难做出预测，尤其是关于未来的预测”，但是数据科学方法的发展使我们能够在大型数据集中构建越来越有效的预测模型。该博士学位项目将应用机器学习和深度学习方法以及经典的统计模型，这是对英国超过500,000人的令人难以置信的健康研究。该学生将整合遗传，环境和临床数据，以预测与英国衰老人群（如心脏病和癌症）相关的疾病发作。一个特定的重点是评估遗传信息的效用：遗传学是否为常规收集的临床和生物标志物数据增加信息，并且遗传学在临床预测算法中可以扮演什么角色？在第1年，学生将发展他们的编程，分析和“大数据”技能，建立经典的统计模型和机器学习算法，以评估临床数据（包括生物透镜和血液生物标志物）的预测能力，生活方式数据（例如吸烟习惯，饮食，饮食和运动）以及在冠状动脉疾病中的遗传倾向。在第二年，将使用机器学习方法及其预测能力以及所有其他信息来源来建立新的遗传风险评分，并使用机器学习方法及其预测能力。此外，机器/深度学习方法将用于确定新的环境风险因素。在第三年，学生将建立全面的疾病风险模型，并针对金标准的临床预测工具测试其预测能力。