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·玻尔（Niels Bohr）曾说过：“很难做出预测，尤其是对未来的预测”，但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。这个博士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，英国生物银行，一个令人难以置信的健康研究超过50万人在英国。该学生将整合遗传，环境和临床数据，以预测与英国老龄化人口相关的疾病的发病，如心脏病和癌症。一个特别的重点将是评估遗传信息的效用：遗传学是否为非常规收集的临床和生物标志物数据增加了信息，以及遗传学在临床预测算法中发挥了什么作用？在第一年，学生将培养他们的编程，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（包括生物识别和血液生物标志物），生活方式数据（如吸烟习惯，饮食和运动）和冠状动脉疾病的遗传易感性的预测能力。在第二年，将使用机器学习方法为不同的疾病建立新的遗传风险评分，并结合所有其他信息来源评估其预测能力。此外，机器/深度学习方法将用于识别新的环境风险因素。在第三年，学生将建立全面的疾病风险模型，并测试他们对黄金标准的临床预测工具的预测能力。