Unsupervised meta-clustering identifies risk clusters in acute myeloid leukemia based on clinical and genetic profiles

BACKGROUND: Increasingly large and complex biomedical data sets challenge conventional hypothesis-driven analytical approaches, however, data-driven unsupervised learning can detect inherent patterns in such data sets. METHODS: While unsupervised analysis in the medical literature commonly only util...

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Detalles Bibliográficos
Autores principales: Eckardt, Jan-Niklas, Röllig, Christoph, Metzeler, Klaus, Heisig, Peter, Stasik, Sebastian, Georgi, Julia-Annabell, Kroschinsky, Frank, Stölzel, Friedrich, Platzbecker, Uwe, Spiekermann, Karsten, Krug, Utz, Braess, Jan, Görlich, Dennis, Sauerland, Cristina, Woermann, Bernhard, Herold, Tobias, Hiddemann, Wolfgang, Müller-Tidow, Carsten, Serve, Hubert, Baldus, Claudia D., Schäfer-Eckart, Kerstin, Kaufmann, Martin, Krause, Stefan W., Hänel, Mathias, Berdel, Wolfgang E., Schliemann, Christoph, Mayer, Jiri, Hanoun, Maher, Schetelig, Johannes, Wendt, Karsten, Bornhäuser, Martin, Thiede, Christian, Middeke, Jan Moritz
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10192332/
https://www.ncbi.nlm.nih.gov/pubmed/37198246
http://dx.doi.org/10.1038/s43856-023-00298-6

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10192332/
https://www.ncbi.nlm.nih.gov/pubmed/37198246
http://dx.doi.org/10.1038/s43856-023-00298-6

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