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Toward a fractalomic idiotype/anti-idiotypic paradigm

The CoViD-19 pandemic has demonstrated the need for future developments in anti-viral immunology. We propose that artificial intelligence (AI) and machine learning, and in particular fractal analysis could play a crucial role in that context. Fractals - never-ending repeats of self-similar shapes wh...

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Detalles Bibliográficos
Autores principales: Chiappelli, Francesco, Penhaskashi, Jaden
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Biomedical Informatics 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10326330/
https://www.ncbi.nlm.nih.gov/pubmed/37426493
http://dx.doi.org/10.6026/97320630018730
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author Chiappelli, Francesco
Penhaskashi, Jaden
author_facet Chiappelli, Francesco
Penhaskashi, Jaden
author_sort Chiappelli, Francesco
collection PubMed
description The CoViD-19 pandemic has demonstrated the need for future developments in anti-viral immunology. We propose that artificial intelligence (AI) and machine learning, and in particular fractal analysis could play a crucial role in that context. Fractals - never-ending repeats of self-similar shapes whose composite tend to resemble the whole - are found in most natural biological structures including immunoglobulin and antigenic epitopes. Increased knowledge of the fractalomic properties of the idiotype/anti-idiotypic paradigm should help develop a novel and improved simplified artificial model of the immune system. Case in point, the regulation and dampening of antibodies as well as the synergetic recognition of an antigen by multiple idiotypes are both immune mechanisms that require further analysis. An enhanced understanding of these complexities could lead to better data analysis for novel vaccines to improve their sensitivity and specificity as well as open other new doors in the field of immunology.
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spelling pubmed-103263302023-07-08 Toward a fractalomic idiotype/anti-idiotypic paradigm Chiappelli, Francesco Penhaskashi, Jaden Bioinformation Editorial The CoViD-19 pandemic has demonstrated the need for future developments in anti-viral immunology. We propose that artificial intelligence (AI) and machine learning, and in particular fractal analysis could play a crucial role in that context. Fractals - never-ending repeats of self-similar shapes whose composite tend to resemble the whole - are found in most natural biological structures including immunoglobulin and antigenic epitopes. Increased knowledge of the fractalomic properties of the idiotype/anti-idiotypic paradigm should help develop a novel and improved simplified artificial model of the immune system. Case in point, the regulation and dampening of antibodies as well as the synergetic recognition of an antigen by multiple idiotypes are both immune mechanisms that require further analysis. An enhanced understanding of these complexities could lead to better data analysis for novel vaccines to improve their sensitivity and specificity as well as open other new doors in the field of immunology. Biomedical Informatics 2022-09-30 /pmc/articles/PMC10326330/ /pubmed/37426493 http://dx.doi.org/10.6026/97320630018730 Text en © 2022 Biomedical Informatics https://creativecommons.org/licenses/by/3.0/This is an Open Access article which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. This is distributed under the terms of the Creative Commons Attribution License.
spellingShingle Editorial
Chiappelli, Francesco
Penhaskashi, Jaden
Toward a fractalomic idiotype/anti-idiotypic paradigm
title Toward a fractalomic idiotype/anti-idiotypic paradigm
title_full Toward a fractalomic idiotype/anti-idiotypic paradigm
title_fullStr Toward a fractalomic idiotype/anti-idiotypic paradigm
title_full_unstemmed Toward a fractalomic idiotype/anti-idiotypic paradigm
title_short Toward a fractalomic idiotype/anti-idiotypic paradigm
title_sort toward a fractalomic idiotype/anti-idiotypic paradigm
topic Editorial
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10326330/
https://www.ncbi.nlm.nih.gov/pubmed/37426493
http://dx.doi.org/10.6026/97320630018730
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