Data mining epidemiological relationships

我们的目标是开发和使用尖端数据挖掘工具来识别导致常见疾病的风险因素，以及可以预防或治疗这些疾病的潜在药物靶点。在MRC综合流行病学单位内开发的方法使用遗传数据来帮助确定可以修改以降低疾病风险或影响的生活方式风险因素，还可以确定潜在的药物靶点。该方案正在开发工具和数据库，以使这种类型的分析自动化，并将其应用于大规模人口数据集，以帮助我们发现预防和治疗疾病的新方法。我们还将这些分析的证据与其他类型的生物医学信息结合在一个“知识图谱”中，以使我们能够调查疾病的潜在机制，确定新的治疗或预防目标，预测药物的副作用，并确定将现有药物用于其他疾病的机会。我们正在开发的方法、软件和知识图谱向研究界公开提供，以最大限度地发挥其改善人口健康的潜力。

We aim to develop and use cutting edge data mining tools to identify risk factors that cause common diseases and potential drug targets that could prevent or treat these diseases. Methods developed within the MRC Integrative Epidemiology Unit use genetic data to help identify lifestyle risk factors that could be modified to reduce the risk or impact of disease, and can also identify potential drug targets. This programme is developing tools and databases to automate this type of analysis and apply it to large-scale population datasets to help us discover new ways to prevent and treat disease. We are also combining the evidence from these analyses with other types of biomedical information in a “knowledge graph” to enable us to investigate the mechanisms underlying disease, identify new targets for treatment or prevention, predict side effects of drugs and identify opportunities to repurpose existing drugs for other diseases. The methods, software and knowledge graph we are developing are made openly available to the research community to maximise their potential to improve population health.