TY  - JOUR
AU  - Chulian, Salvador
AU  - Martinez-Rubio, Alvaro
AU  - Perez-Garcia, Victor M
AU  - Rosa, Maria
AU  - Blazquez Goñi, Cristina
AU  - Rodriguez Gutierrez, Juan Francisco
AU  - Hermosin-Ramos, Lourdes
AU  - Molinos Quintana, Agueda
AU  - Caballero-Velazquez, Teresa
AU  - Ramirez-Orellana, Manuel
AU  - Castillo Robleda, Ana
AU  - Fernandez-Martinez, Juan Luis
PY  - 2020
DO  - 10.3390/cancers13010017
SN  - 2072-6694
UR  - http://hdl.handle.net/10668/16863
T2  - Cancers
AB  - Artificial intelligence methods may help in unveiling information that is hidden in high-dimensional oncological data. Flow cytometry studies of haematological malignancies provide quantitative data with the potential to be used for the construction...
LA  - en
PB  - MDPI
KW  - Acute lymphoblastic leukaemia
KW  - CD38
KW  - Fisher’s ratio
KW  - Flow cytometry data
KW  - Mathematical oncology
KW  - Personalised medicine
KW  - Response biomarkers
KW  - Flow cytometry
KW  - Artificial intelligence
KW  - Precursor celllLymphoblastic leukemia-lymphoma
KW  - Recurrence
KW  - Hematologic neoplasms
KW  - Computational biology
KW  - Probability
KW  - Biomarkers
TI  - High-Dimensional Analysis of Single-Cell Flow Cytometry Data Predicts Relapse in Childhood Acute Lymphoblastic Leukaemia.
TY  - Research article
VL  - 13
ER  -