Application of an Exploratory Knowledge-Discovery Pipeline Based on Machine Learning to Multi-Scale OMICS Data to Characterise Myocardial Injury in a Cohort of Patients with Septic Shock: An Observational Study

Currently, there is no therapy targeting septic cardiomyopathy (SC), a key contributor to organ dysfunction in sepsis. In this study, we used a machine learning (ML) pipeline to explore transcriptomic, proteomic, and metabolomic data from patients with septic shock, and prospectively collected measu...

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Detalles Bibliográficos
Autores principales: Bollen Pinto, Bernardo, Ribas Ripoll, Vicent, Subías-Beltrán, Paula, Herpain, Antoine, Barlassina, Cristina, Oliveira, Eliandre, Pastorelli, Roberta, Braga, Daniele, Barcella, Matteo, Subirats, Laia, Bauzá-Martinez, Julia, Odena, Antonia, Ferrario, Manuela, Baselli, Giuseppe, Aletti, Federico, Bendjelid, Karim
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8509561/
https://www.ncbi.nlm.nih.gov/pubmed/34640372
http://dx.doi.org/10.3390/jcm10194354

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8509561/
https://www.ncbi.nlm.nih.gov/pubmed/34640372
http://dx.doi.org/10.3390/jcm10194354

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