Data mining epidemiological relationships: integration of causal analysis with published evidence

流行病学中的因果推理侧重于确定导致疾病的危险因素。既定的方法侧重于可能影响特定疾病的特定风险因素。然而，现在存在的丰富的生物医学数据使我们能够评估广泛的风险因素网络与疾病之间的因果关系。通过考虑这种关系的更广泛的网络，我们将确定不同风险因素的相对重要性以及针对这些风险因素的干预措施的潜在副作用。我们还将把生物学数据（如分子途径、药物靶点）与因果关系结合起来，使我们能够了解导致疾病的分子机制，并确定潜在的药物和公共卫生干预措施。这些数据和关系将结合在一个专门建立的“图形”数据库中，并将开发方法来挖掘新的因果风险因素和潜在的干预措施。我们在这个项目中为我们的研究整理的数据将对研究界具有广泛的价值。我们将为其他研究人员提供一个开放和可访问的软件平台，以搜索和使用我们为他们自己的研究集成的各种数据集。

Causal inference in epidemiology focuses on identifying the risk factors that cause disease. Established approaches focus on specific risk factors that may impact on specific diseases. However, the wealth of biomedical data that now exist enable us to assess the causal relationships between a broad network of risk factors and diseases. By considering a much wider network of such relationships we will establish the relative importance of different risk factors and the potential side-effects of interventions that target those risk factors. We will also integrate biological data (eg molecular pathways, drug targets) with causal relationships to enable us to understand the molecular mechanisms that lead to disease, and identify potential pharmaceutical and public health interventions. These data and relationships will be combined in a purpose-built “graph” database and methods will be developed to mine for novel causal risk factors and potential interventions. The data that we collate for our research within this programme will have wide-reaching value to the research community. We will provide an open and accessible software platform for other researchers to search and use the various datasets we have integrated for their own research.