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

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

https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_11432235

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

About this item

Full title

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

Publisher

Switzerland: MDPI AG

Journal title

International journal of molecular sciences, 2024-09, Vol.25 (18), p.9988

Language

English

Formats

Publication information

Publisher

Switzerland: MDPI AG

More information

Scope and Contents

Contents

In immunoglobulin G (IgG),
-glycosylation plays a pivotal role in structure and function. It is often altered in different diseases, suggesting that it could be a promising health biomarker. Studies indicate that IgG glycosylation not only associates with various diseases but also has predictive capabilities. Additionally, changes in IgG glycosy...

Alternative Titles

Full title

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

Identifiers

Primary Identifiers

Record Identifier

TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_11432235

Permalink

https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_11432235

Other Identifiers

ISSN

1422-0067,1661-6596

E-ISSN

1422-0067

DOI

10.3390/ijms25189988

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