Cargando…

Cracking the genetic code with neural networks

The genetic code is textbook scientific knowledge that was soundly established without resorting to Artificial Intelligence (AI). The goal of our study was to check whether a neural network could re-discover, on its own, the mapping links between codons and amino acids and build the complete deciphe...

Descripción completa

Detalles Bibliográficos
Autores principales: Joiret, Marc, Leclercq, Marine, Lambrechts, Gaspard, Rapino, Francesca, Close, Pierre, Louppe, Gilles, Geris, Liesbet
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10117997/
https://www.ncbi.nlm.nih.gov/pubmed/37091301
http://dx.doi.org/10.3389/frai.2023.1128153
_version_ 1785028713062072320
author Joiret, Marc
Leclercq, Marine
Lambrechts, Gaspard
Rapino, Francesca
Close, Pierre
Louppe, Gilles
Geris, Liesbet
author_facet Joiret, Marc
Leclercq, Marine
Lambrechts, Gaspard
Rapino, Francesca
Close, Pierre
Louppe, Gilles
Geris, Liesbet
author_sort Joiret, Marc
collection PubMed
description The genetic code is textbook scientific knowledge that was soundly established without resorting to Artificial Intelligence (AI). The goal of our study was to check whether a neural network could re-discover, on its own, the mapping links between codons and amino acids and build the complete deciphering dictionary upon presentation of transcripts proteins data training pairs. We compared different Deep Learning neural network architectures and estimated quantitatively the size of the required human transcriptomic training set to achieve the best possible accuracy in the codon-to-amino-acid mapping. We also investigated the effect of a codon embedding layer assessing the semantic similarity between codons on the rate of increase of the training accuracy. We further investigated the benefit of quantifying and using the unbalanced representations of amino acids within real human proteins for a faster deciphering of rare amino acids codons. Deep neural networks require huge amount of data to train them. Deciphering the genetic code by a neural network is no exception. A test accuracy of 100% and the unequivocal deciphering of rare codons such as the tryptophan codon or the stop codons require a training dataset of the order of 4–22 millions cumulated pairs of codons with their associated amino acids presented to the neural network over around 7–40 training epochs, depending on the architecture and settings. We confirm that the wide generic capacities and modularity of deep neural networks allow them to be customized easily to learn the deciphering task of the genetic code efficiently.
format Online
Article
Text
id pubmed-10117997
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-101179972023-04-21 Cracking the genetic code with neural networks Joiret, Marc Leclercq, Marine Lambrechts, Gaspard Rapino, Francesca Close, Pierre Louppe, Gilles Geris, Liesbet Front Artif Intell Artificial Intelligence The genetic code is textbook scientific knowledge that was soundly established without resorting to Artificial Intelligence (AI). The goal of our study was to check whether a neural network could re-discover, on its own, the mapping links between codons and amino acids and build the complete deciphering dictionary upon presentation of transcripts proteins data training pairs. We compared different Deep Learning neural network architectures and estimated quantitatively the size of the required human transcriptomic training set to achieve the best possible accuracy in the codon-to-amino-acid mapping. We also investigated the effect of a codon embedding layer assessing the semantic similarity between codons on the rate of increase of the training accuracy. We further investigated the benefit of quantifying and using the unbalanced representations of amino acids within real human proteins for a faster deciphering of rare amino acids codons. Deep neural networks require huge amount of data to train them. Deciphering the genetic code by a neural network is no exception. A test accuracy of 100% and the unequivocal deciphering of rare codons such as the tryptophan codon or the stop codons require a training dataset of the order of 4–22 millions cumulated pairs of codons with their associated amino acids presented to the neural network over around 7–40 training epochs, depending on the architecture and settings. We confirm that the wide generic capacities and modularity of deep neural networks allow them to be customized easily to learn the deciphering task of the genetic code efficiently. Frontiers Media S.A. 2023-04-06 /pmc/articles/PMC10117997/ /pubmed/37091301 http://dx.doi.org/10.3389/frai.2023.1128153 Text en Copyright © 2023 Joiret, Leclercq, Lambrechts, Rapino, Close, Louppe and Geris. 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
Joiret, Marc
Leclercq, Marine
Lambrechts, Gaspard
Rapino, Francesca
Close, Pierre
Louppe, Gilles
Geris, Liesbet
Cracking the genetic code with neural networks
title Cracking the genetic code with neural networks
title_full Cracking the genetic code with neural networks
title_fullStr Cracking the genetic code with neural networks
title_full_unstemmed Cracking the genetic code with neural networks
title_short Cracking the genetic code with neural networks
title_sort cracking the genetic code with neural networks
topic Artificial Intelligence
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10117997/
https://www.ncbi.nlm.nih.gov/pubmed/37091301
http://dx.doi.org/10.3389/frai.2023.1128153
work_keys_str_mv AT joiretmarc crackingthegeneticcodewithneuralnetworks
AT leclercqmarine crackingthegeneticcodewithneuralnetworks
AT lambrechtsgaspard crackingthegeneticcodewithneuralnetworks
AT rapinofrancesca crackingthegeneticcodewithneuralnetworks
AT closepierre crackingthegeneticcodewithneuralnetworks
AT louppegilles crackingthegeneticcodewithneuralnetworks
AT gerisliesbet crackingthegeneticcodewithneuralnetworks