graph neural network

MoCoO: Momentum Contrast ODE-Regularized VAE for Single-Cell Trajectory Inference and Representation Learning

MoCoO improves single-cell trajectory inference by combining VAE, Neural ODE, MoCo, and Flow Matching, achieving top performance in clustering and embedding quality across multiple datasets.

Morphology-Guided Deep Learning for Nanoparticle Agglomeration Diagnostic Assays

This paper presents a morphology-guided deep learning platform for nanoparticle agglomeration assays, achieving high sensitivity and recall for DNA and SARS-CoV-2 RNA detection.

MAMGL: A memory-augmented meta-graph learning framework for adolescent major depression disorder diagnosis

This paper introduces BrainMetaGCN, a memory-augmented graph learning framework for AMDD diagnosis using rs-fMRI, achieving better performance than baseline models.

Accurate Protein-Protein Interactions Modeling through Physics-informed Geometric Invariant Learning

ProTact improves protein-protein interaction prediction using a geometric graph neural network with physics-informed constraints, outperforming existing methods on benchmark datasets.

GraphHDBSCAN*: Graph-based Hierarchical Clustering on High Dimensional Single-cell RNA Sequencing Data

GraphHDBSCAN* performs hierarchical clustering on scRNA-seq data, recovering biologically meaningful hierarchies and improving flat partition quality.

Predicting Unseen Gene Perturbation Response Using Graph Neural Networks with Biological Priors

This paper introduces PerturbGraph, a graph neural network framework that predicts transcriptional responses to unseen gene perturbations by integrating biological priors, outperforming existing methods on benchmark datasets.

Beyond Regional Activations: Structural Connectivity Message-Passing Shallow Neural Networks for Brain Decoding

This paper introduces a shallow neural network with structural connectivity message-passing for brain decoding, achieving 83.0% classification accuracy using fMRI data from 30 subjects.

Learning gene interactions from tabular gene expression data using Graph Neural Networks

This paper introduces REGEN, a GNN framework for learning gene interaction networks from bulk transcriptomic data and predicting patient outcomes, showing improved performance across cancer types.

GRNFormer: Accurate Gene Regulatory Network Inference Using Graph Transformer

GRNFormer infers gene regulatory networks from transcriptomics data using a graph transformer framework, outperforming existing methods and recovering known and novel regulatory interactions.

A graph-based deep learning framework for diabetic retinopathy classification with topology-aware feature augmentation

This paper introduces a graph-based deep learning framework for diabetic retinopathy classification that incorporates topological features and relational graph learning, achieving higher accuracy than standard CNNs.

Solving the Diagnostic Odyssey with Synthetic Phenotype Data

The paper introduces a framework to generate synthetic phenotype data using HPO structure and trains a GNN to prioritize disease genes, outperforming existing methods on real datasets.

ChiMER: Integrating chromatin architecture into splicing graphs for chimeric enhancer RNAs detection

ChiMER detects enhancer-exon fusion transcripts by integrating chromatin architecture into splicing graphs, improving sensitivity over conventional methods.

Mutation-centric Network Construction using Long-Range Interactions

The paper introduces a graph-based framework to construct mutation-centric networks using long-range genomic interactions, demonstrating its ability to cluster cancer subtypes and prioritize non-coding driver mutations.

Graph Neural Networks Model Based on Atomic Hybridization for Predicting Drug Targets

This paper develops a hybrid deep learning model combining graph neural networks and molecular descriptors to predict drug targets with high accuracy and interpretability.

SpotGraphs: Graph-based analysis of spatially resolved transcriptional data in R

SpotGraphs provides a flexible R package for directly interacting with spatially resolved transcriptomic data through graph analysis, offering tools for quality filtering and adjacency editing.

ACENet: a graph neural network for predicting enzyme pHmin using structure and sequence insights

ACENet predicts enzyme pHmin using a graph neural network integrating structural and evolutionary features, achieving high accuracy and stability across conditions.

SpatioCAD: Context-aware graph diffusion model for pinpointing spatially variable genes in heterogeneous tissues

SpatioCAD detects spatially variable genes by accounting for cell density variations, improving accuracy in heterogeneous tissues like tumors.

Modeling Multi-Modal Brain Connectomes for Brain Disorder Diagnosis via Graph Diffusion Optimal Transport Network

This paper introduces GDOT-Net, a graph diffusion optimal transport network that aligns structural and functional brain connectivity for improved diagnosis of neuropsychiatric disorders.

AMR-GNN: A multi-representation graph neural network framework to enable genomic antimicrobial resistance prediction

AMR-GNN predicts antimicrobial resistance using a graph neural network framework that integrates multiple genomic representations from whole-genome sequencing data.

t2pmhc: A Structure-Informed Graph Neural Network to predict TCR-pMHC Binding

The paper introduces t2pmhc, a structure-informed graph neural network for predicting TCR-pMHC binding, showing improved generalization to unseen peptides compared to existing methods.

PAMG-AT: A Physiological Attention Multi-Graph Model with Adaptive Topology for Stress Detection using Wearable Devices

This paper introduces PAMG-AT, a hierarchical graph neural network for stress detection using wearable sensors, achieving high accuracy and providing interpretability through attention mechanisms.

Predicting pyrazinamide resistance in Mycobacterium tuberculosis using a graph convolutional network

This study uses a graph convolutional network to predict pyrazinamide resistance in Mycobacterium tuberculosis by integrating protein structure and residue-level features, achieving high accuracy.

Unified Protein-Small Molecule Graph Neural Networks for Binding Site Prediction

YuelPocket predicts small molecule binding sites on proteins using a graph neural network with a global graph design, achieving high AUC-ROC scores on benchmark datasets.

Decoding the Microbiome-Disease Axis with Interpretable Graph Neural Networks

This paper introduces GIM, an interpretable graph neural network for microbiome-disease prediction, achieving high accuracy while providing mechanistic insights into microbial interactions.

BiGAT-Fusion: Node-Wise Gated Bidirectional Graph Attention for Drug Repurposing

BiGAT-Fusion improves drug-disease association prediction by addressing class imbalance, directional asymmetry, and static feature fusion through a novel graph neural network model.

GTA-5: A Unified Graph Transformer Framework for Ligands and Protein Binding Sites - Part I: Constructing the PDB Pocket and Ligand Space

GTA-5 embeds protein binding pockets and ligands into latent spaces to enable structural reasoning, showing functional clustering and physicochemical property capture.

Informative Missingness in Nominal Data: A Graph-Theoretic Approach to Revealing Hidden Structure

The paper presents a graph-theoretic approach to analyze missing data in nominal datasets, revealing hidden structures by treating missingness as informative signals.

SLECA: a single-cell atlas of systemic lupus erythematosus enabling rare cell discovery using graph transformer

SLECA creates a single-cell atlas of SLE to identify rare cell types, finding expanded double-negative T cells linked to disease severity and showing potential for therapeutic reprogramming.

Computational nanobody design using graph neural networks and Metropolis Monte Carlo sampling

This paper develops a computational method using graph neural networks and Metropolis Monte Carlo sampling to design high-affinity nanobodies, achieving a Pearson correlation of 0.62 on benchmark.

Graph topology reframes the coherence of cell-state manifold inference under heterogeneous single-cell observations

The paper identifies how heterogeneous single-cell observations distort manifold-based inference and proposes topological stability descriptors to assess reliability.