graph neural network

IsoGraph resolves coordinated transcript choice and gene abundance in the aging human brain

IsoGraph identifies age-associated transcript-usage signals in human brain transcriptomes, distinguishing them from gene abundance and cellular composition effects.

GenDiMug: a generic inclusive multigraph framework to analyze genomic organizations and -omics data at multiple scales

GenDiMug is a multigraph framework for analyzing genomic organization and multi-omics data across scales, revealing gene expression clumping and circadian regulation in a photosynthetic alga.

FlexENN: A Graph Neural Network for Binding Energy Prediction of Globular and Intrinsically Disordered Proteins

The paper introduces FlexENN, a graph neural network that predicts binding energies for both folded and intrinsically disordered protein complexes, showing improved performance over rigid-body methods.

Protein Language Model-Conditioned Graph Neural Networks for Multitask Ligand Activity Prediction Across Human GPCRs

The paper develops a graph neural network that combines ligand molecular graphs with protein language model embeddings to predict GPCR ligand activity, showing improved performance over baseline models.

scProfiterole: Clustering of Single-Cell Proteomic DataUsing Graph Contrastive Learning via Spectral Filters

The paper introduces scProfiterole, a framework for clustering single-cell proteomic data using graph contrastive learning with spectral filters, showing improved cell type identification.

GRASS-MIL: graph-based representation and discovery of phenotype-associated spatial structures with multiple instance learning

GRASS-MIL predicts phenotypes from spatial single-cell data and decomposes predictions into specific cellular neighborhoods, showing improved performance over composition-based methods.

SIMORGH: Ensemble-Aware Geometric Deep Learning for Apo-State and Cryptic Ligand Binding Site Prediction

SIMORGH predicts ligand binding sites from apo proteins by integrating conformational ensembles, outperforming existing methods in ligand-free settings and identifying cryptic pockets.

PGRanker: Enhancing Phenotype Knowledge with Graph Learning for Mendelian Gene Prioritization

PGRanker improves Mendelian gene prioritization by integrating phenotype data with gene networks using graph learning, achieving higher recall and better ranking of causal genes.

Distilling Trial-Level EEG Variability into Participant Representations: Neurophysiologically Constrained Graph Learning in ADHD

The paper develops a neurophysiologically constrained graph learning method to extract stable participant representations from EEG data in ADHD and finds group-related structure in embeddings that capture trial-level variability.

HyperCom: a hypergraph-based method to infer cell-resolved cell-cell communication

HyperCom infers cell-resolved cell-cell communication using hypergraphs from single-cell transcriptomics data, providing a continuous sender-receiver score for ligand-receptor pairs.

Tractable and Thermodynamically Consistent pKa Prediction via First-Dissociation Aggregation

This paper introduces DTi-pKa, a graph neural network that predicts pKa values with thermodynamic consistency and achieves high accuracy on benchmark datasets.

MemBack: An Equivariant Graph Neural Network for Backmapping Lipid Membranes

MemBack reconstructs atomistic lipid structures from coarse-grained simulations with high accuracy and preserves structural organization across resolutions.

A Graph-Attentive GAN for Rare-Cell-Aware Single-Cell RNA-Seq Data Generation

GARAGE generates realistic synthetic scRNA-seq data by addressing high-dimensional, small-sample and rare-cell challenges using graph attention and adversarial generation.

Reliable Evaluation of Transductive Population Graph Neural Networks for Multisite Autism fMRI

Site-aware population GNN on ABIDE-I: cohort AUC about 0.94 (kept with site-only edges) was not reproduced by a site-prior classifier (0.506); a directed-edge support gain (+0.252) vanished when symmetrised (−0.003).

ST-DISTAL: Dual-Branch Graph Convolution with Distributional Alignment for Cell-Type Deconvolution

ST-DISTAL improves cell-type deconvolution in spatial transcriptomics by integrating spatial and molecular information through a dual-branch graph convolutional framework.

Branch-Wise Regularization for Heterogeneous GNN-Based MALDI-TOF AMR Prediction with Biomarker-Consensus Edges

This paper evaluates a heterogeneous GNN for AMR prediction from MALDI-TOF spectra, showing it performs better than MLP only at high spectral similarity.

Decoding brain anatomy from neuronal neighborhoods with MYCEL

The paper introduces MYCEL, a graph neural network that improves decoding of brain anatomy from neuronal neighborhoods by learning local pooling, outperforming fixed aggregation on multiple datasets.

SpatialTRACE predicts anatomical axes and regions in spatial transcriptomics and microscopy

SpatialTRACE extends anatomical annotations from a few structures to tissue-wide maps using graph and image-based models in spatial transcriptomics and microscopy.

BiomiX 3.0: A user-friendly platform for democratized multi-omics integration with graph-based learning.

BiomiX 3.0 is a multi-omics integration platform that introduces PRAMIGO, a supervised heterogeneous graph transformer, and benchmarks five methods on CLL and PTB datasets, showing that no single method captures all biologically relevant signals.

CSGDA: A Cell State-Guided Graph Domain Adaptation Network for Single-Cell Drug Response Prediction

CSGDA predicts drug responses across biological heterogeneities by addressing distribution shifts using a cell state-guided graph domain adaptation framework.

Advancing Wildlife Image Analysis: A Graph Attention Contrastive Learning Approach for Region-Specific Mammal Classification

This paper develops a region-specific AI model for mammal classification using graph attention contrastive learning, achieving high accuracy in recognizing local species and improving data cleaning efficiency.

Learning Environment-Associated Marker Rankings with Attention-Based Graph Neural Networks

This paper introduces a graph neural network to estimate environment-associated marker rankings from crop trial data and evaluates its performance on maize datasets.

SurfGraphPro: Integrating Protein Language Models with Geometric Deep Learning On Coarse Protein Surfaces for Binding Site Prediction

SurfGraphPro predicts protein binding sites by integrating protein language models with geometric deep learning on coarse protein surfaces, achieving faster performance than existing methods.

Graph-based pangenome of Venturia inaequalis, the apple scab fungus, reveals structural variants associated with population differentiation

The paper constructs a graph-based pangenome of Venturia inaequalis, identifies structural variants linked to population differentiation, and reveals gene repertoire conservation and virulence-associated diversity.

Extracting interpretable single-cell metabolic states with graph-guided representation learning

The paper introduces MeRN, a graph-guided variational autoencoder for learning single-cell metabolic states from transcriptomes, and identifies metabolic drivers in various biological contexts.

Structurally Restricted Message-Passing within Shallow Architectures for Explainable Network-Level Brain Decoding on Small Cohorts

This paper proposes a structurally constrained message-passing framework for brain decoding on small datasets, achieving competitive performance while maintaining interpretability.

An interpretable deep learning framework for classifying neuronal morphologies using topology and graph neural networks

This paper presents a deep learning framework that classifies neuronal morphologies using topology and graph neural networks, achieving accuracy comparable to human experts.

SPECTRA: predicting cellular perturbation responses with Graph Learning over Gene Regulatory Networks

SPECTRA predicts cellular perturbation responses using graph learning on gene regulatory networks, improving recovery of sparse transcriptional effects compared to latent-shift models.

A Graph-based QSAR Modeling Pipeline for Predicting In vitro PubChem Assays and In vivo Human Hepatotoxicity: Mechanistic Analysis of Caspase-3/7 Activation

This paper develops a graph-based QSAR pipeline to predict in vitro Caspase-3/7 activation and in vivo hepatotoxicity, showing improved performance over classical models.

StressNET: an adaptable deep-learning model for mechanical stress inference in tissues

StressNET infers intercellular mechanical stresses from tissue geometry using a Graph Neural Network, achieving state-of-the-art performance in zebrafish and Xenopus embryos.