Classifying Antibodies Using Flow Cytometry Data: Class Prediction and Class Discovery

Classifying monoclonal antibodies, based on the similarity of their binding to the proteins (antigens) on the surface of blood cells, is essential for progress in immunology, hematology and clinical medicine. The collaborative efforts of researchers from many countries have led to the classification...

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Bibliographic Details
Published in:	Biometrical journal Vol. 47; no. 5; pp. 740 - 754
Main Authors:	Salganik, M. P., Milford, E. L., Hardie, D. L., Shaw, S., Wand, M. P.
Format:	Journal Article
Language:	English
Published:	Berlin WILEY-VCH Verlag 01-10-2005 WILEY‐VCH Verlag Wiley-VCH
Subjects:	Antibodies, Monoclonal - classification Antibody Specificity Applications Applied sciences Biology, psychology, social sciences Cell Line, Tumor Class discovery Class prediction Classification Computer science; control theory; systems Control theory. Systems Dissimilarity measure Double-Blind Method Exact sciences and technology Flow cytometry Flow Cytometry - methods Flow Cytometry - statistics & numerical data Fluorescent Dyes Humans Kernel smoothing Mathematics Medical sciences Monoclonal antibodies Predictive Value of Tests Probability and statistics Probability theory and stochastic processes Sciences and techniques of general use SiZer Special processes (renewal theory, markov renewal processes, semi-markov processes, statistical mechanics type models, applications) Statistics Supervised learning System theory Unsupervised learning Biometrics Flow cytometry Process computer Class discovery Monoclonal antibody Data flow Immunology SiZer Classification Computer Smoothing methods Dissimilarity measure Life science Class prediction Interpretation Unsupervised learning Protein Kernel method Medicine Statistical method Cell population Supervised learning Monoclonal antibodies Random set Planning Kernel smoothing Algorithm analysis
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