acescGlycoAI: Machine learning analysis of single-cell glycan and RNA sequencing data for cell-type annotation and malignancy-associated glycan discovery
Single-cell glycan and RNA sequencing (scGR-seq) enables characterization of cellular glycan heterogeneity. Here, we present scGlycoAI, an interpretable machine learning framework that integrates scGR-seq data for glycan-centered biological discovery. Using lectin-binding profiles as input, scGlycoAI employs hierarchical machine learning to accurately classify cell types and subtypes within complex tumor microenvironments. Applied to pancreatic ductal adenocarcinoma (PDAC) datasets, scGlycoAI achieved an overall…
Using only glycan lectin-binding features, the model sorted pancreatic cancer cells into eight classes and singled out PVL lectin binding as a marker of aggressive cancer states.
The survival finding is an association in the TCGA cohort, and the authors only suggest the framework could support future biomarker discovery and glycan-targeted therapy development.
Evidence
- Peer-reviewedGlycobiology2026-10-24
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Truvace Impact Record TRV-2026-2263, v1: “scGlycoAI: Machine learning analysis of single-cell glycan and RNA sequencing data for cell-type annotation and malignancy-associated glycan discovery.” Truvace, 2026-10-26. /record/TRV-2026-2263 (accessed at citation time). sha256 72db609719dbcf4a…
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