Model selection for metabolomics: predicting diagnosis of coronary artery disease using automated machine learning

MOTIVATION: Selecting the optimal machine learning (ML) model for a given dataset is often challenging. Automated ML (AutoML) has emerged as a powerful tool for enabling the automatic selection of ML methods and parameter settings for the prediction of biomedical endpoints. Here, we apply the tree-b...

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Detalles Bibliográficos
Autores principales: Orlenko, Alena, Kofink, Daniel, Lyytikäinen, Leo-Pekka, Nikus, Kjell, Mishra, Pashupati, Kuukasjärvi, Pekka, Karhunen, Pekka J, Kähönen, Mika, Laurikka, Jari O, Lehtimäki, Terho, Asselbergs, Folkert W, Moore, Jason H
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2019
Materias:
Original Papers
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7703753/
https://www.ncbi.nlm.nih.gov/pubmed/31702773
http://dx.doi.org/10.1093/bioinformatics/btz796

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7703753/
https://www.ncbi.nlm.nih.gov/pubmed/31702773
http://dx.doi.org/10.1093/bioinformatics/btz796

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