Dataset Information

Improving the Accuracy and Efficiency of Core Fucose Identification Using Machine Learning

ABSTRACT: In this study, we employed artificial intelligence to address the challenges in identifying core fucose due to migration effects. By knocking out the FUT8 gene in normal mouse brains, we ensured accurate labeling of non-core fucosylated glycans, enabling the identification of mannose glycans with core fucosylation in wild-type mouse brains. We developed two machine learning models—a semi-supervised mapping convergence (MC) model and a self-supervised autoencoder (AE) model—for core fucose recognition. Experimental results demonstrated that both models performed exceptionally well, with the MC model showing potential in identifying non-core fucosylated glycans and the AE model excelling in core fucose detection.

INSTRUMENT(S):

ORGANISM(S): Mus Musculus (mouse)

TISSUE(S): Brain

SUBMITTER: Shisheng Sun

LAB HEAD: Shisheng Sun

PROVIDER: PXD062880 | Pride | 2025-09-29

REPOSITORIES: Pride

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Dataset's files

Source:

			Action	DRS
	Fut8_KO_hilic_brain_1.raw	Raw
	Fut8_KO_hilic_brain_1_result.xlsx	Xlsx
	Fut8_KO_hilic_brain_2.raw	Raw
	Fut8_KO_hilic_brain_2_result.xlsx	Xlsx
	Fut8_KO_hilic_brain_3.raw	Raw

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Publications

Core fucose identification in glycoproteomics: an ML approach addressing fucose migration in mass spectrometry.

Su Yuanjie Y Jiang Chang C Yang Ziyue Z Sun Shisheng S Zhang Junying J

Bioinformatics advances 20250915 1

<h4>Motivation</h4>Core fucosylation is a common type of glycosylation that plays a significant role in biological functions. Accurate identification of core fucosylated glycopeptides is challenging due to fucose migration phenomenon during mass spectrometry. By using glycopeptides from mouse brain with FUT8 knocked out as cases and core-fucosylated high-mannose glycans in normal mouse brain as controls, the phenomena are widely observed from mass spectrometry data. The relative intensities of 1 ...[more]

PMID: 40980549

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