Predictive modeling and reliable biomarker discovery in clinical omics studies
Predictive modeling and reliable biomarker discovery in clinical omics studies
High-content omic technologies coupled machine learning methods have transformed the biomarker discovery process. However, the translation of computational results into scalable clinical biomarkers remains challenging. A rate-limiting step is the rigorous selection of reliable biomarker candidates among a host of biological features. Drawing examples from real-world clinical omics studies, I will introduce Stabl, a general machine learning framework that identifies a sparse, reliable set of biomarkers by integrating noise injection and a data-driven signal-to-noise threshold into multivariable predictive modeling.
Julien Hédou
CEO, SurgeCare
Julien Hédou studied Engineering at Ecole Polytechnique (Paris) before completing his MS in Biomedical Informatics degree from Stanford University. Julien has been working on developing the machine learning pipelines of the Gaudillière lab that integrates high parameter mass cytometry and proteomics using sparse machine learning approaches to identify biologically plausible and reliable predictive biomarkers, focusing on several clinical scenarios: 1) immune mechanisms of surgical recovery and complications, 2) feto-maternal health outcomes, including preterm birth, preeclampsia and endometriosis, 3) immune dysfunction and outcomes prediction in patients with acute ischemic stroke. Julien Hédou is now CEO of SurgeCare, a life science company that commercializes, PreCyte®, for predicting the risk of postoperative complications, and its laboratory services, SurgeLab™, for acquiring individualized immune signatures and analyzing multiomic datasets.
Predictive modeling and reliable biomarker discovery in clinical omics studies
A presentation by Julien Hédou
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