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A Machine Learning Approach for High-Dimensional Time-to-Event Prediction With Application to Immunogenicity of Biotherapies in the ABIRISK Cohort
Predicting immunogenicity for biotherapies using patient and drug-related factors represents nowadays a challenging issue. With the growing ability to collect massive amount of data, machine learning algorithms can provide efficient predictive tools. From the bio-clinical data collected in the multi...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7154163/ https://www.ncbi.nlm.nih.gov/pubmed/32318076 http://dx.doi.org/10.3389/fimmu.2020.00608 |
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author | Duhazé, Julianne Hässler, Signe Bachelet, Delphine Gleizes, Aude Hacein-Bey-Abina, Salima Allez, Matthieu Deisenhammer, Florian Fogdell-Hahn, Anna Mariette, Xavier Pallardy, Marc Broët, Philippe |
author_facet | Duhazé, Julianne Hässler, Signe Bachelet, Delphine Gleizes, Aude Hacein-Bey-Abina, Salima Allez, Matthieu Deisenhammer, Florian Fogdell-Hahn, Anna Mariette, Xavier Pallardy, Marc Broët, Philippe |
author_sort | Duhazé, Julianne |
collection | PubMed |
description | Predicting immunogenicity for biotherapies using patient and drug-related factors represents nowadays a challenging issue. With the growing ability to collect massive amount of data, machine learning algorithms can provide efficient predictive tools. From the bio-clinical data collected in the multi-cohort of autoimmune diseases treated with biotherapies from the ABIRISK consortium, we evaluated the predictive power of a custom-built random survival forest for predicting the occurrence of anti-drug antibodies. This procedure takes into account the existence of a population composed of immune-reactive and immune-tolerant subjects as well as the existence of a tiny expected proportion of relevant predictive variables. The practical application to the ABIRISK cohort shows that this approach provides a good predictive accuracy that outperforms the classical survival random forest procedure. Moreover, the individual predicted probabilities allow to separate high and low risk group of patients. To our best knowledge, this is the first study to evaluate the use of machine learning procedures to predict biotherapy immunogenicity based on bioclinical information. It seems that such approach may have potential to provide useful information for the clinical practice of stratifying patients before receiving a biotherapy. |
format | Online Article Text |
id | pubmed-7154163 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-71541632020-04-21 A Machine Learning Approach for High-Dimensional Time-to-Event Prediction With Application to Immunogenicity of Biotherapies in the ABIRISK Cohort Duhazé, Julianne Hässler, Signe Bachelet, Delphine Gleizes, Aude Hacein-Bey-Abina, Salima Allez, Matthieu Deisenhammer, Florian Fogdell-Hahn, Anna Mariette, Xavier Pallardy, Marc Broët, Philippe Front Immunol Immunology Predicting immunogenicity for biotherapies using patient and drug-related factors represents nowadays a challenging issue. With the growing ability to collect massive amount of data, machine learning algorithms can provide efficient predictive tools. From the bio-clinical data collected in the multi-cohort of autoimmune diseases treated with biotherapies from the ABIRISK consortium, we evaluated the predictive power of a custom-built random survival forest for predicting the occurrence of anti-drug antibodies. This procedure takes into account the existence of a population composed of immune-reactive and immune-tolerant subjects as well as the existence of a tiny expected proportion of relevant predictive variables. The practical application to the ABIRISK cohort shows that this approach provides a good predictive accuracy that outperforms the classical survival random forest procedure. Moreover, the individual predicted probabilities allow to separate high and low risk group of patients. To our best knowledge, this is the first study to evaluate the use of machine learning procedures to predict biotherapy immunogenicity based on bioclinical information. It seems that such approach may have potential to provide useful information for the clinical practice of stratifying patients before receiving a biotherapy. Frontiers Media S.A. 2020-04-07 /pmc/articles/PMC7154163/ /pubmed/32318076 http://dx.doi.org/10.3389/fimmu.2020.00608 Text en Copyright © 2020 Duhazé, Hässler, Bachelet, Gleizes, Hacein-Bey-Abina, Allez, Deisenhammer, Fogdell-Hahn, Mariette, Pallardy and Broët. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Immunology Duhazé, Julianne Hässler, Signe Bachelet, Delphine Gleizes, Aude Hacein-Bey-Abina, Salima Allez, Matthieu Deisenhammer, Florian Fogdell-Hahn, Anna Mariette, Xavier Pallardy, Marc Broët, Philippe A Machine Learning Approach for High-Dimensional Time-to-Event Prediction With Application to Immunogenicity of Biotherapies in the ABIRISK Cohort |
title | A Machine Learning Approach for High-Dimensional Time-to-Event Prediction With Application to Immunogenicity of Biotherapies in the ABIRISK Cohort |
title_full | A Machine Learning Approach for High-Dimensional Time-to-Event Prediction With Application to Immunogenicity of Biotherapies in the ABIRISK Cohort |
title_fullStr | A Machine Learning Approach for High-Dimensional Time-to-Event Prediction With Application to Immunogenicity of Biotherapies in the ABIRISK Cohort |
title_full_unstemmed | A Machine Learning Approach for High-Dimensional Time-to-Event Prediction With Application to Immunogenicity of Biotherapies in the ABIRISK Cohort |
title_short | A Machine Learning Approach for High-Dimensional Time-to-Event Prediction With Application to Immunogenicity of Biotherapies in the ABIRISK Cohort |
title_sort | machine learning approach for high-dimensional time-to-event prediction with application to immunogenicity of biotherapies in the abirisk cohort |
topic | Immunology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7154163/ https://www.ncbi.nlm.nih.gov/pubmed/32318076 http://dx.doi.org/10.3389/fimmu.2020.00608 |
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