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.
Preview |
Text
Biometrical_J_2024_Servius..pdf - Published Version Download (1MB) | Preview |
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 |
Archive Staff Only
 |
View Item |
