Article

GRNFormer: Accurate Gene Regulatory Network Inference Using Graph Transformer

2025-01-27

Abstract excerpt

<h4>Motivation</h4> Deciphering gene regulatory networks (GRNs) from single-cell transcriptomics data remains a fundamental challenge in computational biology. It is hindered by data sparsity, high dimensionality, and the lack of scalable, generalizable inference models. To address this, we present GRNFormer, a generalizable graph transformer framework for accurate GRN inference from transcriptomics data across...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 7231d93c-20f3-5171-b3dd-7dca54124a3b
DOI: 10.1101/2025.01.26.634966

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

GRNFormer: Accurate Gene Regulatory Network Inference Using Graph TransformerDOI 10.1101/2025.01.26.634966Read

Graph attention network for link prediction of gene regulations from single-cell RNA-sequencing dataSemantic proximity Read

RegFormer: A Single-Cell Foundation Model Powered by Gene Regulatory HierarchiesSemantic proximity Read

scGRIP: a graph-based explainable AI framework for single-cell multi-omics Gene Regulatory Inference with Prior KnowledgeSemantic proximity Read

FloREN: Decoding Immune Regulatory Networks through Interpretable Graph Transformer Patient RepresentationsSemantic proximity Read

MultiGRNFormer: A Transformer-Based Multi-Omics GRN Inference FrameworkSemantic proximity Read

scRegulate: Single-Cell Regulatory-Embedded Variational Inference of Transcription Factor Activity from Gene ExpressionSemantic proximity Read

scDirect: key transcription factor identification for directing cell state transitions based on single-cell multi-omics dataSemantic proximity Read

Transcriptome Graph Transformer--A Graph Transformer-Based Unsupervised Model for Transcriptome Data AnalysisSemantic proximity Read

Regulatory Logic in Larger Single-Cell Foundation Models: A Sparse Autoencoder Comparison of scFoundation and GeneformerSemantic proximity Read

Regulatory Logic in Larger Single-Cell Foundation Models: A Sparse Autoencoder Comparison of scFoundation and GeneformerSemantic proximity Read

scEMB: Learning context representation of genes based on large-scale single-cell transcriptomicsSemantic proximity Read

Single-cell gene regulatory network reconstruction and key regulator identification using a dual-channel fusion graph convolutional networkSemantic proximity Read