Machine learning for classification of hypertension subtypes using multi-omics: A multi-centre, retrospective, data-driven study

Parminder S. Reel (Lead / Corresponding author), Smarti Reel, Josie C.. van Kralingen, Katharina Langton, Katharina Lang, Zoran Erlic, Casper K. Larsen, Laurence Amar, Christina Pamporaki, Paolo Mulatero, Anne Blanchard, Marek Kabat, Stacy Robertson, Scott M. MacKenzie, Angela E. Taylor, Mirko Peitzsch, Filippo Ceccato, Carla Scaroni, Martin Reincke, Matthias KroissMichael C. Dennedy, Alessio Pecori, Silvia Monticone, Jaap Deinum, Gian Paolo Rossi, Livia Lenzini, John D. McClure, Thomas Nind, Alexandra Riddell, Anthony Stell, Christian Cole, Isabella Sudano, Cornelia Prehn, Jerzy Adamski, Anne Paule Gimenez-Roqueplo, Guillaume Assié, Wiebke Arlt, Felix Beuschlein, Graeme Eisenhofer, Eleanor Davies, Maria Christina Zennaro, Emily Jefferson (Lead / Corresponding author)

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Omics-Based Strategies for Improved Diagnosis and Treatment of Endocrine Hypertension (ENSAT-HT) (Joint with Inserm, University of Torino, University of Padua, University of Glasgow, University of Birmingham, Radboud University Medical Centre, SleekIT Limited and Inserm Transfert)
Connell, J. (Investigator), Doney, A. (Investigator), Jefferson, E. (Investigator) & Zhou, K. (Investigator)
COMMISSION OF THE EUROPEAN COMMUNITIES
1/05/15 → 31/12/21
Project: Research

Machine learning for classification of hypertension subtypes using multi-omics: A multi-centre, retrospective, data-driven study

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Omics-Based Strategies for Improved Diagnosis and Treatment of Endocrine Hypertension (ENSAT-HT) (Joint with Inserm, University of Torino, University of Padua, University of Glasgow, University of Birmingham, Radboud University Medical Centre, SleekIT Limited and Inserm Transfert)