FloReMi: Flow density survival regression using minimal feature redundancy

Cytometry Part A

Volumn 89, Issue 1, 2016, Pages 22-29

  Van Gassen, Sofie ^a,b,c   Vens, Celine ^b,c,d   Dhaene, Tom ^a   Lambrecht, Bart N ^b,c   Saeys, Yvan ^b,c  

^a GHENT UNIVERSITY   (Belgium)

^b DEPARTMENT OF PLANT SYSTEMS BIOLOGY   (Belgium)

^c GHENT UNIVERSITY HOSPITAL   (Belgium)

^d UNIVERSITY OF LEUVEN   (Belgium)

Author keywords

Feature selection; Machine learning; Polychromatic flow cytometry; Survival time prediction

Indexed keywords

ACQUIRED IMMUNODEFICIENCY SYNDROME; ALGORITHM; BENCHMARKING; BIOLOGY; CLUSTER ANALYSIS; CYTOLOGY; DISEASE COURSE; FLOW CYTOMETRY; HUMAN; HUMAN IMMUNODEFICIENCY VIRUS INFECTION; MACHINE LEARNING; MORTALITY; PATHOLOGY; PROCEDURES; REGRESSION ANALYSIS; STAINING; STATISTICAL ANALYSIS; T LYMPHOCYTE;

ACQUIRED IMMUNODEFICIENCY SYNDROME; ALGORITHMS; BENCHMARKING; CLUSTER ANALYSIS; COMPUTATIONAL BIOLOGY; DATA INTERPRETATION, STATISTICAL; DISEASE PROGRESSION; FLOW CYTOMETRY; HIV SEROPOSITIVITY; HUMANS; MACHINE LEARNING; REGRESSION ANALYSIS; STAINING AND LABELING; T-LYMPHOCYTES;

EID: 84983191355     PISSN: 15524922     EISSN: 15524930     Source Type: Journal    
DOI: 10.1002/cyto.a.22734     Document Type: Note

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