Bayesian Networks and Integrative Semiotic Models in Precision Medicine
Presented at the 5th Annual BayesiaLab Conference in Paris, September 25–October 4, 2017.
Abstract
Discovery and assessment of biomarkers are new challenges for the revolution of precision medicine. Moreover, the development of “multi-omics” arrays and next-generation sequencing technologies have identified sets of markers that could be translated as signatures for clinical stratification of diseases. These signatures could optimize therapeutic management or assess better the risk to have or to develop diseases and help practitioners' decisions to perform deep (invasive or costly) diagnosis procedures. So, Bayesian statistical methods and modeling techniques provide a powerful approach to refine the probability of outcome and decision making in clinical practice. Bayesian networks through BayesiaLab offer the opportunity to search the better more informative factors or a combination of these factors for enhancing the prediction of endpoints. As an example, we present some applications for risk prediction and personalized management in the field of prostate cancer. In this way, Bayesian approaches, using BayesiaLab have been shown a powerful strategy to explore, validate, and translate to clinical practice useful multifactorial predictors for personalized medicine.
Presentation Video
About the Presenter
Olivier Cussenot, M.D., Ph.D.Professor at the Pierre & Marie Curie Faculty of Medicine
Sorbonne University
- Full Professor at the Pierre & Marie Curie Faculty of Medicine, Sorbonne University.
- Head of the Department of Urology (University Hospital Paris-Est, APHP).
- Director of a Research Group on onco-urology at the Institute of Cancerology (Sorbonne University).
- Surgeon, fully qualified in general surgery, urology, oncology & medical genetics.
- Honorary member of the "Institut Universitaire de France”.
- Chairman of the Strategic Committee for Research in Urology at the French National Cancer Institute.