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Predicting class switch recombination in B-cells from antibody repertoire data

Servius, Lutecia; Pigoli, Davide; Ng, Joseph; Fraternali, Franca; (2024) Predicting class switch recombination in B-cells from antibody repertoire data. Biometrical Journal , 66 (4) , Article 2300171. 10.1002/bimj.202300171. Green open access

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Abstract

Statistical and machine learning methods have proved useful in many areas of immunology. In this paper, we address for the first time the problem of predicting the occurrence of class switch recombination (CSR) in B-cells, a problem of interest in understanding antibody response under immunological challenges. We propose a framework to analyze antibody repertoire data, based on clonal (CG) group representation in a way that allows us to predict CSR events using CG level features as input. We assess and compare the performance of several predicting models (logistic regression, LASSO logistic regression, random forest, and support vector machine) in carrying out this task. The proposed approach can obtain an unweighted average recall of (Formula presented.) with models based on variable region descriptors and measures of CG diversity during an immune challenge and, most notably, before an immune challenge.

Type: Article
Title: Predicting class switch recombination in B-cells from antibody repertoire data
Location: Germany
Open access status: An open access version is available from UCL Discovery
DOI: 10.1002/bimj.202300171
Publisher version: http://dx.doi.org/10.1002/bimj.202300171
Language: English
Additional information: © 2024 The Authors. Biometrical Journal published by Wiley-VCH GmbH. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
Keywords: Science & Technology, Life Sciences & Biomedicine, Physical Sciences, Mathematical & Computational Biology, Statistics & Probability, Mathematics, balanced accuracy, clonal groups, immune responses, predictive models, AFFINITY, REVEALS, MODELS
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences > Div of Biosciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences > Div of Biosciences > Structural and Molecular Biology
URI: https://discovery.ucl.ac.uk/id/eprint/10201029
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