Patient similarity by joint matrix trifactorization to identify subgroups in acute myeloid leukemia

OBJECTIVE: Computing patients’ similarity is of great interest in precision oncology since it supports clustering and subgroup identification, eventually leading to tailored therapies. The availability of large amounts of biomedical data, characterized by large feature sets and sparse content, motiv...

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Detalles Bibliográficos
Autores principales: Vitali, F, Marini, S, Pala, D, Demartini, A, Montoli, S, Zambelli, A, Bellazzi, R
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2018
Materias:
Research and Applications
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6951984/
https://www.ncbi.nlm.nih.gov/pubmed/31984320
http://dx.doi.org/10.1093/jamiaopen/ooy008

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6951984/
https://www.ncbi.nlm.nih.gov/pubmed/31984320
http://dx.doi.org/10.1093/jamiaopen/ooy008

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