ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2025
scHeteroNet: A Heterophily-Aware Graph Neural Network for Accurate Cell Type Annotation and Novel Cell Detection.
Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
6 citing papers in PubMed.
- Specificity-driven graph learning for the identification of rare cell states.Advanced biotechnology · 2026Article
- MethyAnno: An Interpretable Automated Annotation Method Leveraging Multi-Scale Information and Metric Learning Framework for scDNAm Data.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Specificity-driven cell-gene graph learning identifies rare cell states in single-cell and spatial transcriptomic data.Advanced biotechnology · 2026Article
- GatorSC: multi-scale cell and gene graphs with mixture-of-experts fusion for single-cell transcriptomics.Briefings in bioinformatics · 2026Article
- GatorSC: Multi-Scale Cell and Gene Graphs with Mixture-of-Experts Fusion for Single-Cell Transcriptomics.bioRxiv : the preprint server for biology · 2025Article
- scHeteroNet: A Heterophily-Aware Graph Neural Network for Accurate Cell Type Annotation and Novel Cell Detection.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Single-cell RNA sequencing (scRNA-seq) has unveiled extensive cellular heterogeneity, yet precise cell type annotation and the identification of novel cell populations remain significant challenges. scHeteroNet, a novel graph neural network framework specifically designed to leverage heterophily in scRNA-seq data, is presented. Unlike traditional methods that assume homophily, scHeteroNet captures complex cell-cell interactions by integrating information from both immediate and extended cellular neighborhoods, resulting in highly accurate cell representations. Additionally, scHeteroNet incorporates an innovative novelty propagation mechanism that robustly detects previously uncharacterized cell types. Comprehensive evaluations across diverse scRNA-seq datasets demonstrate that scHeteroNet consistently outperforms state-of-the-art approaches in both cell type classification and novel cell detection. This heterophily-aware approach enhances the ability to uncover cellular diversity, providing deeper insights into complex biological systems and advancing the field of single-cell analysis.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.