Background
One of the current challenges of precision medicine is to integrate heterogeneous data for the most adequate description of the patient. Today, biomedical data come from multiple sources (biomic data, imaging, microbiota, clinical notes, drug prescriptions, claim databases…), each data type being structured in a specific way. These available data can also be enriched with a priori information. For example, it is possible to link the biomic data to interaction graphs, the imaging data to features known to be relevant for diagnosis, the microbiota to functional annotations, or prescriptions to drug knowledge bases.
Objectives
The co-supervised PhD project aims at developing methods for the analysis of patients’ data harnessing prior knowledge for better performances. We will focus on enhancing i) biomic and ii) medical and administrative data available for patients using prior knowledge. Knowledge integration will be based on semantic web technologies or more broadly on knowledge graphs, widely used by the community to structure information. The aim is to quantify the contribution of this a priori information to classical risk analysis models as well as to more complex dimension reduction models, such as auto-encoders.
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