Computational approaches to understand disease mechanisms and to advance patient diagnosis
Peter Bram 't Hoen's research group develops computational approaches that exploit molecular –omics data in combination with clinical data to better understand disease mechanisms and to advance patient diagnosis and stratification for therapeutic and life style interventions.read more
Computational approaches to understand disease mechanisms and to advance patient diagnosisWe develop computational pipelines for the (integrated) analysis of genomics, transcriptomics, proteomics, metabolomics and microbiome data. We adopt statistical and knowledge-driven data integration approaches to discover sets of diagnostic and prognostic biomarkers. Our focus on interoperability of data and services facilitates the interpretation of in-house data (such as those from patient samples) in the context of external data sources. In more fundamental research projects, we aim for a better understanding of the impact of DNA variants on RNA and protein sequences, protein structure and clinical phenotypes. We strongly believe that enabling bleeding-edge and high-quality research requires us to work in a multidisciplinary environment and in close collaboration with researchers from other themes and departments. We therefore have a strong network in the Radboudumc with more clinically-oriented partners to ensure that we keep working on topics directly relevant for the local clinicians and their patient. Also outside the Radboudumc we keep close ties with other medical centers such as Leiden and Wageningen, and embrace connections with industry partners. We are active contributers to national infrastructures such as HealthRI, ELIXIR-NL, BBMRI-NL and DTL, to keep up with the most recent developments in the field and to make sure that what we do also finds interest outside of the hospital.
Research group leader
Peter-Bram 't Hoen PhD
AimsOur main objective is to translate biomedical problems into computational solutions and to facilitate the use of molecular (-omics) data in the clinic.
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