Predicting Biochemical and Physiological Parameters: Deep Learning from IgG Glycome Composition.


Journal

International journal of molecular sciences
ISSN: 1422-0067
Titre abrégé: Int J Mol Sci
Pays: Switzerland
ID NLM: 101092791

Informations de publication

Date de publication:
16 Sep 2024
Historique:
received: 17 07 2024
revised: 29 08 2024
accepted: 11 09 2024
medline: 29 9 2024
pubmed: 28 9 2024
entrez: 28 9 2024
Statut: epublish

Résumé

In immunoglobulin G (IgG),

Identifiants

pubmed: 39337475
pii: ijms25189988
doi: 10.3390/ijms25189988
pii:
doi:

Substances chimiques

Immunoglobulin G 0
Polysaccharides 0
Biomarkers 0
glycosylated IgG 0
Glycoproteins 0

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Subventions

Organisme : Croatian Science Foundation
ID : IP-2022-10-1358

Auteurs

Ana Vujić (A)

Department of Biology, Faculty of Science, University of Zagreb, 10000 Zagreb, Croatia.

Marija Klasić (M)

Department of Biology, Faculty of Science, University of Zagreb, 10000 Zagreb, Croatia.

Gordan Lauc (G)

Genos Glycoscience Research Laboratory, 10000 Zagreb, Croatia.
Faculty of Pharmacy and Biochemistry, University of Zagreb, 10000 Zagreb, Croatia.

Ozren Polašek (O)

Department of Public Health, University of Split School of Medicine, 21000 Split, Croatia.
Croatian Science Foundation, 10000 Zagreb, Croatia.

Vlatka Zoldoš (V)

Department of Biology, Faculty of Science, University of Zagreb, 10000 Zagreb, Croatia.

Aleksandar Vojta (A)

Department of Biology, Faculty of Science, University of Zagreb, 10000 Zagreb, Croatia.
Genos Glycoscience Research Laboratory, 10000 Zagreb, Croatia.

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Classifications MeSH