A robust machine learning framework to identify signatures for frailty: a nested case-control study in four aging European cohorts

David Gomez-Cabrero, on behalf of the FRAILOMIC initiative, Stefan Walter, Imad Abugessaisa, Rebeca Miñambres-Herraiz, Lucia Bernad Palomares, Lee Butcher, Jorge D. Erusalimsky, Francisco Jose Garcia-Garcia, José Carnicero, Timothy C. Hardman, Harald Mischak, Petra Zürbig, Matthias Hackl, Johannes Grillari, Edoardo Fiorillo, Francesco Cucca, Matteo Cesari, Isabelle Carrie, Marco ColpoStefania Bandinelli, Catherine Feart, Karine Peres, Jean-François Dartigues, Catherine Helmer, José Viña, Gloria Olaso, Irene García-Palmero, Jorge García Martínez, Pidder Jansen-Dürr, Tilman Grune, Daniela Weber, Giuseppe Lippi, Chiara Bonaguri, Alan J Sinclair, Jesper Tegner, Leocadio Rodriguez-Mañas

Research output: Contribution to journalArticlepeer-review

12 Scopus citations

Fingerprint

Dive into the research topics of 'A robust machine learning framework to identify signatures for frailty: a nested case-control study in four aging European cohorts'. Together they form a unique fingerprint.