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
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

110807

Informations de copyright

© 2024 The Author(s). Published by Elsevier Inc.

Déclaration de conflit d'intérêts

The authors declare no competing interest.

Auteurs

Alexander Sasse (A)

Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA 98195, USA.

Maria Chikina (M)

Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA 16354, USA.

Sara Mostafavi (S)

Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA 98195, USA.
Canadian Institute for Advanced Research, Toronto, ON MG5 1ZB, Canada.

Classifications MeSH