Mathematical modeling of the diversity in human B and T cell receptors using machine learning

26th European Modeling and Simulation Symposium, EMSS 2014

Volumn , Issue , 2014, Pages 164-170

  Schaller, Susanne ^a   Weinberger, Johannes ^b   Danzer, Martin ^b   Gabriel, Christian ^b   Oberbauer, Rainer ^c   Winkler, Stephan M ^a  

^a UNIVERSITY OF APPLIED SCIENCES UPPER AUSTRIA   (Austria)

^b Red Cross Blood Transfusion Service of Upper Austria   (Austria)

^c ELISABETHINEN HOSPITAL   (Austria)

Author keywords

B and T cell diversity analysis; Data mining; Immune system; Model identification; Parameter identification

Indexed keywords

ARTIFICIAL INTELLIGENCE; CELL MEMBRANES; CELLS; CLONE CELLS; CLONING; CYTOLOGY; DATA MINING; IDENTIFICATION (CONTROL SYSTEMS); IMMUNE SYSTEM; LEARNING SYSTEMS;

DIVERSITY ANALYSIS; EMPIRICAL APPROACH; EVOLUTION STRATEGIES; HUMAN IMMUNE SYSTEMS; MACHINE LEARNING METHODS; MODEL IDENTIFICATION; NEXT-GENERATION SEQUENCING; PARAMETER OPTIMIZATION METHODS;

T-CELLS;

EID: 84912099464     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

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