A metabolite-based machine learning approach to diagnose Alzheimer-type dementia in blood: Results from the European Medical Information Framework for Alzheimer disease biomarker discovery cohort
ContributorsStamate, Daniel; Kim, Min; Proitsi, Petroula; Westwood, Sarah; Baird, Alison; Nevado-Holgado, Alejo; Hye, Abdul; Bos, Isabelle; Vos, Stephanie J B; Vandenberghe, Rik; Teunissen, Charlotte E; Kate, Mara Ten; Scheltens, Philip; Gabel, Silvy; Meersmans, Karen; Blin, Olivier; Richardson, Jill; De Roeck, Ellen; Engelborghs, Sebastiaan; Sleegers, Kristel; Bordet, Régis; Ramit, Lorena; Kettunen, Petronella; Tsolaki, Magda; Verhey, Frans; Alcolea, Daniel; Lléo, Alberto; Peyratout, Gwendoline; Tainta, Mikel; Johannsen, Peter; Freund-Levi, Yvonne; Frölich, Lutz; Dobricic, Valerija; Frisoni, Giovanni; Molinuevo, José L; Wallin, Anders; Popp, Julius; Martinez-Lage, Pablo; Bertram, Lars; Blennow, Kaj; Zetterberg, Henrik; Streffer, Johannes; Visser, Pieter J; Lovestone, Simon; Legido-Quigley, Cristina
Published inAlzheimer's & dementia, vol. 5, p. 933-938
Publication date2019
Abstract
Keywords
- Alzheimer's disease
- Biomarkers
- EMIF-AD
- Machine-Learning
- Metabolomics
Research group
Funding
- European Commission - European Medical Information Framework [115372]
Citation (ISO format)
STAMATE, Daniel et al. A metabolite-based machine learning approach to diagnose Alzheimer-type dementia in blood: Results from the European Medical Information Framework for Alzheimer disease biomarker discovery cohort. In: Alzheimer’s & dementia, 2019, vol. 5, p. 933–938. doi: 10.1016/j.trci.2019.11.001
Main files (1)
Article (Published version)
Identifiers
- PID : unige:140840
- DOI : 10.1016/j.trci.2019.11.001
- PMID : 31890857
Commercial URLhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC6928349/
ISSN of the journal2352-8737
