Quick and effective approximation of in silico saturation mutagenesis experiments with first-order taylor expansion.
Artificial intelligence
Biocomputational method
In silico biology
Journal
iScience
ISSN: 2589-0042
Titre abrégé: iScience
Pays: United States
ID NLM: 101724038
Informations de publication
Date de publication:
20 Sep 2024
20 Sep 2024
Historique:
received:
19
07
2024
revised:
08
08
2024
accepted:
20
08
2024
medline:
17
9
2024
pubmed:
17
9
2024
entrez:
17
9
2024
Statut:
epublish
Résumé
To understand the decision process of genomic sequence-to-function models, explainable AI algorithms determine the importance of each nucleotide in a given input sequence to the model's predictions and enable discovery of
Identifiants
pubmed: 39286491
doi: 10.1016/j.isci.2024.110807
pii: S2589-0042(24)02032-7
pmc: PMC11404212
doi:
Types de publication
Journal Article
Langues
eng
Pagination
110807Informations de copyright
© 2024 The Author(s). Published by Elsevier Inc.
Déclaration de conflit d'intérêts
The authors declare no competing interest.