How to predict relapse in leukemia using time series data: A comparative in silico study.
Risk stratification and treatment decisions for leukemia patients are regularly based on clinical markers determined at diagnosis, while measurements on system dynamics are often neglected. However, there is increasing evidence that linking quantitative time-course information to disease outcomes ca...
Guardado en:
Autores principales: | Helene Hoffmann, Christoph Baldow, Thomas Zerjatke, Andrea Gottschalk, Sebastian Wagner, Elena Karg, Sebastian Niehaus, Ingo Roeder, Ingmar Glauche, Nico Scherf |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
Public Library of Science (PLoS)
2021
|
Materias: | |
Acceso en línea: | https://doaj.org/article/fb2c2107ef474a52add0aeb0a6fbdb49 |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
How to predict relapse in leukemia using time series data: A comparative in silico study
por: Helene Hoffmann, et al.
Publicado: (2021) -
Profound leukemia cutis in a patient with relapsed T-cell acute lymphoblastic leukemia
por: Ambika Nohria, BA, et al.
Publicado: (2021) -
Gemtuzumab ozogamicin is efficacious in attaining complete remission in relapsed/refractory acute leukemia prior to hematopoietic cell transplant: A case series
por: Giancarlo Fatobene, et al.
Publicado: (2021) -
linus: Conveniently explore, share, and present large-scale biological trajectory data in a web browser.
por: Johannes Waschke, et al.
Publicado: (2021) -
linus: Conveniently explore, share, and present large-scale biological trajectory data in a web browser
por: Johannes Waschke, et al.
Publicado: (2021)