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

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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.