Using semantics to leverage health and research Big Data

将与健康相关的研究大数据连接起来，可以对它们进行比较，并发现更多的样本/参与者规模以进行分析。大数据的复杂性给集成带来了挑战。表型是一种数据类型，在临床和非临床(例如：（组学）健康研究大数据。术语“表型”用于定义一组医学上和语义上不同的概念，例如特征（例如血糖水平）、医学体征和症状（例如高血糖症）以及疾病（例如2型糖尿病）。为了能够比较大数据之间的值，我们需要知道数据集之间的值是否具有相同的含义，而使用“本体”提供了这样做所需的语义严谨性。有几个本体论描述重叠的表型域，但已经为不同的目的而开发，例如SNOMED CT被NHS使用，人类表型本体论（HPO）被研究数据库使用。如果数据集被编码为不同的本体，或者根本没有本体，那么它们可以通过协调过程链接到一个共同的本体。这涉及到映射来自不同本体的术语，以及自由文本的文本挖掘（TM），以便将原始值与本体代码关联起来。不幸的是，在此过程中存在一些障碍，例如本体和公开可用的本体映射之间的差距，需要适应当前的TM方法，这些方法经过优化，可以在明确定义的领域中表现良好，需要扩展当前的映射和TM方法以处理大数据。在此期间，我将通过使用本体来协调表型数据，创建连接临床和非临床研究大数据的增强能力。这将涉及弥合当前本体的差距，并适应当前最先进的TM和本体映射方法，以便它们针对此上下文进行优化，并可应用于大数据。我开发的方法将是疾病不可知论的，但在第一个实例中，它们将应用于当地感兴趣的疾病领域。莱斯特生物医学研究中心专注于心血管、呼吸系统和生活方式疾病，包括初级和二级保健的数据集，以及包括参与者问卷调查和生物样本数据的临床研究。我将把疾病领域内部和之间的临床研究数据与本地和公开的组学数据联系起来，例如全基因组和表观基因组关联研究。当连接多个研究的数据时，研究参与者完成的特定研究和分层选择同意可能不兼容或不明确。这阻碍了一个统一的临床研究数据集被用来回答一个新的研究问题。一些项目致力于开发同意本体，但目前还没有一个合适的同意本体符合NHS关于收集同意的指导。莱斯特已经共同领导了全球基因组学和健康联盟在这一领域的努力，我将帮助扩展到基于本体的方法，以表示NHS的同意和数据使用条件，以允许同意与新的英国数据保护法的要求保持一致。统一的数据集可以连接到标准化数据的公共来源，以弥合与转化研究的差距。这方面的一个例子是跨学科合作，在人类和小鼠表型本体之间进行映射，以便发现一系列人类表型异常的小鼠疾病模型。当一种疾病没有已知的遗传原因时，进行跨物种表型比较的能力允许发现该疾病的潜在小鼠基因敲除模型。这些跨物种映射已应用于公共标准化数据库，我将研究它们与现实世界健康相关大数据的效用。

Connecting health-related research big data enables them to be compared, and increased sample/participant sizes to be discovered for analysis. Big data pose integration challenges with regards to their complexity. Phenotype is a data type that shows particular variety and variability across clinical and non-clinical (e.g. 'omics) health research big data. The term "phenotype" is used to define an aggregated set of medically and semantically distinct concepts such as a trait (e.g. blood glucose level), medical signs and symptoms (e.g. hyperglycemia), and disease (e.g. type 2 diabetes). To be able to compare values across big data we need to know if the values have the same meaning between datasets and the semantic rigour required to do this is provided by the use of "ontologies". There are several ontologies that describe overlapping phenotype domains but have been developed for different purposes, for example SNOMED CT is used by the NHS and the Human Phenotype Ontology (HPO) is used by research databases. If datasets are coded to different ontologies, or no ontology at all, then they can be linked to a common ontology via a process of harmonisation. This involves mapping terms from different ontologies, and text mining (TM) of free-text to associate the original values with ontology codes. Unfortunately, there are several barriers to this such as gaps in the ontologies and the publically available ontology mappings, the need to adapt current TM approaches which are optimised to perform well in clearly defined areas, and the need to scale current mapping and TM methods to work with big data.During this fellowship I will create enhanced capabilities for connecting clinical and non-clinical research big data by using ontologies to harmonise phenotype data. This will involve bridging the gaps in current ontologies, and adapting current state of the art TM and ontology mapping approaches so they are optimised for this context and can be applied to big data. The approaches I develop will be disease agnostic, however in the first instance they will be applied to disease areas of local interest. The Leicester Biomedical Research Centre focuses on cardiovascular, respiratory and lifestyle diseases and encompasses datasets from primary and secondary care, and clinical research studies which include participant questionnaires and biological sample data. I will connect clinical research data within and between disease areas and with local and publically available 'omics data, for example genome- and epigenome-wide association studies.The study-specific and tiered opt-in consent completed by study participants can be incompatible or ambiguous when connecting data across multiple studies. This blocks a harmonised clinical research dataset being used to answer a new research question. Some projects have worked on developing consent ontologies, but there is not currently a suitable consent ontology that fits with NHS guidance on collecting consent. Leicester already co-leads the Global Alliance for Genomics and Health efforts in this area, which I will help extend towards an ontology-based approach for representing NHS consents and data use conditions, to allow consent harmonisation in line with the requirements of new UK data protection laws.Harmonised datasets can be connected to public sources of standardised data to bridge the gap to translational research. An example of this are cross-disciplinary collaborations that have mapped between human and mouse phenotype ontologies, to allow the discovery of mouse disease models for a collection of human phenotypic abnormalities. Where a disease does not have a known genetic cause, the ability to perform a cross-species phenotype comparison allows potential mouse gene-knockout models for the disease to be discovered. These cross-species mappings have been applied to public standardised databases and I will investigate their utility with real-world health related big data.