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Machine Learning Detects Anti-DENV Signatures in Antibody Repertoire Sequences
Dengue infection is a global threat. As of today, there is no universal dengue fever treatment or vaccines unreservedly recommended by the World Health Organization. The investigation of the specific immune response to dengue virus would support antibody discovery as therapeutics for passive immuniz...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2021
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8542978/ https://www.ncbi.nlm.nih.gov/pubmed/34708197 http://dx.doi.org/10.3389/frai.2021.715462 |
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author | Horst, Alexander Smakaj, Erand Natali, Eriberto Noel Tosoni, Deniz Babrak, Lmar Marie Meier, Patrick Miho, Enkelejda |
author_facet | Horst, Alexander Smakaj, Erand Natali, Eriberto Noel Tosoni, Deniz Babrak, Lmar Marie Meier, Patrick Miho, Enkelejda |
author_sort | Horst, Alexander |
collection | PubMed |
description | Dengue infection is a global threat. As of today, there is no universal dengue fever treatment or vaccines unreservedly recommended by the World Health Organization. The investigation of the specific immune response to dengue virus would support antibody discovery as therapeutics for passive immunization and vaccine design. High-throughput sequencing enables the identification of the multitude of antibodies elicited in response to dengue infection at the sequence level. Artificial intelligence can mine the complex data generated and has the potential to uncover patterns in entire antibody repertoires and detect signatures distinctive of single virus-binding antibodies. However, these machine learning have not been harnessed to determine the immune response to dengue virus. In order to enable the application of machine learning, we have benchmarked existing methods for encoding biological and chemical knowledge as inputs and have investigated novel encoding techniques. We have applied different machine learning methods such as neural networks, random forests, and support vector machines and have investigated the parameter space to determine best performing algorithms for the detection and prediction of antibody patterns at the repertoire and antibody sequence levels in dengue-infected individuals. Our results show that immune response signatures to dengue are detectable both at the antibody repertoire and at the antibody sequence levels. By combining machine learning with phylogenies and network analysis, we generated novel sequences that present dengue-binding specific signatures. These results might aid further antibody discovery and support vaccine design. |
format | Online Article Text |
id | pubmed-8542978 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-85429782021-10-26 Machine Learning Detects Anti-DENV Signatures in Antibody Repertoire Sequences Horst, Alexander Smakaj, Erand Natali, Eriberto Noel Tosoni, Deniz Babrak, Lmar Marie Meier, Patrick Miho, Enkelejda Front Artif Intell Artificial Intelligence Dengue infection is a global threat. As of today, there is no universal dengue fever treatment or vaccines unreservedly recommended by the World Health Organization. The investigation of the specific immune response to dengue virus would support antibody discovery as therapeutics for passive immunization and vaccine design. High-throughput sequencing enables the identification of the multitude of antibodies elicited in response to dengue infection at the sequence level. Artificial intelligence can mine the complex data generated and has the potential to uncover patterns in entire antibody repertoires and detect signatures distinctive of single virus-binding antibodies. However, these machine learning have not been harnessed to determine the immune response to dengue virus. In order to enable the application of machine learning, we have benchmarked existing methods for encoding biological and chemical knowledge as inputs and have investigated novel encoding techniques. We have applied different machine learning methods such as neural networks, random forests, and support vector machines and have investigated the parameter space to determine best performing algorithms for the detection and prediction of antibody patterns at the repertoire and antibody sequence levels in dengue-infected individuals. Our results show that immune response signatures to dengue are detectable both at the antibody repertoire and at the antibody sequence levels. By combining machine learning with phylogenies and network analysis, we generated novel sequences that present dengue-binding specific signatures. These results might aid further antibody discovery and support vaccine design. Frontiers Media S.A. 2021-10-11 /pmc/articles/PMC8542978/ /pubmed/34708197 http://dx.doi.org/10.3389/frai.2021.715462 Text en Copyright © 2021 Horst, Smakaj, Natali, Tosoni, Babrak, Meier and Miho. https://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 | Artificial Intelligence Horst, Alexander Smakaj, Erand Natali, Eriberto Noel Tosoni, Deniz Babrak, Lmar Marie Meier, Patrick Miho, Enkelejda Machine Learning Detects Anti-DENV Signatures in Antibody Repertoire Sequences |
title | Machine Learning Detects Anti-DENV Signatures in Antibody Repertoire Sequences |
title_full | Machine Learning Detects Anti-DENV Signatures in Antibody Repertoire Sequences |
title_fullStr | Machine Learning Detects Anti-DENV Signatures in Antibody Repertoire Sequences |
title_full_unstemmed | Machine Learning Detects Anti-DENV Signatures in Antibody Repertoire Sequences |
title_short | Machine Learning Detects Anti-DENV Signatures in Antibody Repertoire Sequences |
title_sort | machine learning detects anti-denv signatures in antibody repertoire sequences |
topic | Artificial Intelligence |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8542978/ https://www.ncbi.nlm.nih.gov/pubmed/34708197 http://dx.doi.org/10.3389/frai.2021.715462 |
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