PXD062880-1

PXD062880 is an original dataset announced via ProteomeXchange.

Title	Improving the Accuracy and Efficiency of Core Fucose Identification Using Machine Learning
Description	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.
HostingRepository	PRIDE
AnnounceDate	2025-09-29
AnnouncementXML	Submission_2025-09-28_16:18:50.081.xml
DigitalObjectIdentifier
ReviewLevel	Peer-reviewed dataset
DatasetOrigin	Original dataset
RepositorySupport	Unsupported dataset by repository
PrimarySubmitter	Shisheng Sun
SpeciesList	scientific name: Mus musculus (Mouse); NCBI TaxID: 10090;
ModificationList	No PTMs are included in the dataset
Instrument	Orbitrap Fusion Lumos

Revision	Datetime	Status	ChangeLog Entry
0	2025-04-12 18:15:25	ID requested
⏵ 1	2025-09-28 16:18:50	announced

10.1093/bioadv/vbaf186;

Su Y, Jiang C, Yang Z, Sun S, Zhang J, Core fucose identification in glycoproteomics: an ML approach addressing fucose migration in mass spectrometry. Bioinform Adv, 5(1):vbaf186(2025) [pubmed]

submitter keyword: Core fucose, Mouse brain,Intact glycopeptides, FUT8 knockout

Shisheng Sun
contact affiliation	Northwest University
contact email	suns@nwu.edu.cn
lab head
Shisheng Sun
contact affiliation	Northwest University
contact email	sun_glycolab@126.com
dataset submitter

Dataset FTP location NOTE: Most web browsers have now discontinued native support for FTP access within the browser window. But you can usually install another FTP app (we recommend FileZilla) and configure your browser to launch the external application when you click on this FTP link. Or otherwise, launch an app that supports FTP (like FileZilla) and use this address: ftp://ftp.pride.ebi.ac.uk/pride/data/archive/2025/09/PXD062880

PRIDE project URI

• PRIDE
  1. PXD062880
     1. Label: PRIDE project
     2. Name: Improving the Accuracy and Efficiency of Core Fucose Identification Using Machine Learning