Predictive analytics of integrated genomic and clinical data using machine learning and complex statistical approaches

大规模的电子健康记录(EHR)数据有可能改变我们对疾病病因和临床风险的理解，为人口规模的临床决策和卫生政策提供重要信息。协调多个数据来源，包括遗传、表观遗传和临床数据，可以极大地提高我们对疾病风险的理解。这种大数据可以为精确医学算法的开发以及与疾病相关的候选基因的新发现提供独特的机会；为将基因发现整合到医学的临床应用中提供一个框架。虽然大规模的电子健康记录资源已被用于风险预测和遗传关联的确定，但对这些数据的分析有限，没有利用数据的多维和纵向丰富性。最近英国大规模生物数据资源的发展需要灵活的分析方法的并行开发，以实现这些丰富数据集的全部潜力。机器学习方法提供了一个框架，通过识别和利用最佳预测结果的特征，从数据本身学习不同变量和类型数据之间的关系。这种方法可能会比传统的统计方法更有优势，因为在传统的统计方法中，变量之间的关系需要预先指定，而且一次只能对有限数量的因素进行建模。相比之下，机器学习方法不仅允许对临床风险进行无模型预测，而且还有助于更好地了解大量潜在预测因素中哪些因素影响临床风险。这项建议侧重于统计和验证的开发和验证，以及利用复杂数据灵活预测多个疾病领域的患者预后风险的机器学习方法。此外，这些方法的应用还可以更好地了解与疾病相关的临床和遗传风险因素。这项工作将特别侧重于系统地评估和扩展当前用于风险预测的统计方法，以纳入机器学习方法，该方法可以灵活地使用大规模遗传和临床数据对复杂的风险模式进行建模，同时适当地考虑风险因素的时间依赖背景(例如重复测量)。该项目的第一阶段将涉及公共可用生物数据资源的整合、管理和协调，如英国生物库、英国基因组学和Interval Study。包括基因表达和功能数据在内的更多数据将被分层，以开发丰富的多维数据集。在没有直接测量的情况下，可以使用已公布的推算和深度学习方法，从序列数据中合理地预测这些数据。在EHR以前工作的基础上，将使用复杂的统计方法来开发特定疾病领域的预测算法。下一阶段将涉及对现有机器和可以对风险进行建模的深度学习方法的评估。然后，这些方法将被扩展到对包含随时间重复测量的纵向数据进行建模。这些方法的预测准确性将使用独立的数据集进行评估。为了了解疾病的遗传病因学，将比较经典的GWAS方法与集成机器学习的方法，以允许对最重要的风险遗传和临床预测因子进行优先排序。该项目将提供一个广泛的分析框架，用于在大数据分析的背景下进行临床风险预测和确定遗传与疾病的关联。从长远来看，这将有助于发展多维数据高通量分析方面的研究能力和专业知识，以支持临床决策，并改善患者的健康。

Large-scale electronic health record (EHR) data has the potential to transform our understanding of disease aetiology and clinical risk, providing important information for clinical decision making and health policy at population scale. Aligning multiple sources of data, including genetic, epigenetic and clinical data can substantially improve our understanding of disease risk. Such big data can provide unique opportunities for development of algorithms for precision medicine as well as for novel discovery of candidate genes associated with disease; providing a framework for integration of genetic discovery into clinical applications in medicine. While large-scale EHR resources have been utilised for risk prediction, and identification of genetic associations, analyses of these data have been limited and have not harnessed the multi-dimensional and longitudinal richness of data. The recent development of large-scale biodata resources within the UK necessitates the parallel development of flexible analytic methods that can realise the full potential of these rich datasets.Machine learning methods provide a framework whereby the relationships among different variables and types of data are learnt from the data itself by identifying and utilising features that best predict outcomes. Such approaches may provide an advantage over classical statistical methods, where the relationships among variables require pre-specification, and only a limited number of factors can be modelled at a time. By contrast, machine learning methods not only allow model-free prediction of clinical risk, but also help better understand which factors, among large numbers of potential predictors, influence clinical risk.This proposal focuses on development and validation of statistical, and machine learning approaches that utilise complex data to flexibly predict the risk of patient outcomes across multiple disease areas. Additionally, application of such methods also allows a better understanding of clinical and genetic risk factors associated with disease. This work will specifically focus on systematically evaluating and extending current statistical methods for risk prediction to incorporate machine learning approaches that can model complex patterns of risk using large-scale genetic and clinical data flexibly, while appropriately accounting for the time-dependent context of risk factors (e.g. repeated measurements).The first phase of the project will involve integration, curation and harmonisation of publicly available biodata resources, such as the UK Biobank, Genomics England and INTERVAL study. Further data, including gene expression, and functional data will be layered to develop a rich multi-dimensional dataset. These data can be reasonably predicted from sequence data, when not directly measured, using published imputation and deep learning approaches. Developing on previous work with EHRs, complex statistical approaches will be used to develop predictive algorithms for specific disease areas. The next stage will involve evaluation of existing machine and deep learning approaches that can model risk. These approaches will then be extended to model longitudinal data incorporating repeated measurements over time. The predictive accuracy of these approaches will be evaluated using independent datasets. To understand the genetic aetiology of disease, classical GWAS approaches will be compared with approaches that integrate machine learning to allow prioritisation of the most important genetic and clinical predictors of risk.This project will provide a broad analytic framework for clinical risk prediction and identification of genetic associations with disease in the context of big data analytics. In the longer term, this will contribute to a programme of developing research capacity and expertise in high throughput analytics of multi-dimensional data with the aim of supporting clinical decision making, and improving patient health.