Classifying antibodies using flow cytometry data: class prediction and class discovery

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dc.contributor.author Salganik, Mkhail en_US
dc.contributor.author Milford, Edgar en_US
dc.contributor.author Hardie, D. en_US
dc.contributor.author Shaw, S. en_US
dc.contributor.author Wand, Matt en_US
dc.contributor.editor en_US
dc.date.accessioned 2011-02-07T06:17:50Z
dc.date.available 2011-02-07T06:17:50Z
dc.date.issued 2005 en_US
dc.identifier 2010000145 en_US
dc.identifier.citation Salganik M. et al. 2005, 'Classifying antibodies using flow cytometry data: class prediction and class discovery', Wiley - VCH Verlag GmbH & Co. KGaA, vol. 47, no. 5, pp. 740-754. en_US
dc.identifier.issn 0323-3847 en_US
dc.identifier.other C1UNSUBMIT en_US
dc.identifier.uri http://hdl.handle.net/10453/12989
dc.description.abstract 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 of thousands of antibodies into 247 clusters of differentiation (CD). Classification is based on flow cytometry and biochemical data. In preliminary classifications of antibodies based on flow cytometry data, the object requiring classification (an antibody) is described by a set of random samples from unknown densities of fluorescence intensity. An individual sample is collected in the experiment, where a population of cells of a certain type is stained by the identical fluorescently marked replicates of the antibody of interest. Samples are collected for multiple cell types. The classification problems of interest include identifying new CDs (class discovery or unsupervised learning) and assigning new antibodies to the known CD clusters (class prediction or supervised learning). These problems have attracted limited attention from statisticians. We recommend a novel approach to the classification process in which a computer algorithm suggests to the analyst the subset of the ?most appropriate? classifications of an antibody in class prediction problems or the ?most similar? pairs/groups of antibodies in class discovery problems. The suggested algorithm speeds up the analysis of a flow cytometry data by a factor 10?20. This allows the analyst to focus on the interpretation of the automatically suggested preliminary classification solutions and on planning the subsequent biochemical experiments en_US
dc.language en_US
dc.publisher Wiley - VCH Verlag GmbH & Co. KGaA en_US
dc.relation.isbasedon http://dx.doi.org/10.1002/bimj.200310142 en_US
dc.title Classifying antibodies using flow cytometry data: class prediction and class discovery en_US
dc.parent Biometrical Journal en_US
dc.journal.volume 47 en_US
dc.journal.number 5 en_US
dc.publocation Germany en_US
dc.identifier.startpage 740 en_US
dc.identifier.endpage 754 en_US
dc.cauo.name SCI.Mathematical Sciences en_US
dc.conference Verified OK en_US
dc.for 010400 en_US
dc.personcode 0000064920 en_US
dc.personcode 0000064921 en_US
dc.personcode 0000064922 en_US
dc.personcode 0000064923 en_US
dc.personcode 110509 en_US
dc.percentage 100 en_US
dc.classification.name Statistics en_US
dc.classification.type FOR-08 en_US
dc.edition en_US
dc.custom en_US
dc.date.activity en_US
dc.location.activity en_US
dc.description.keywords * Classification; * Monoclonal antibodies; * Flow cytometry; * Dissimilarity measure; * Kernel smoothing; * SiZer; * Class discovery; * Class prediction; * Unsupervised learning; * Supervised learning en_US
dc.staffid 110509 en_US


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