graph neural network

GnnDebugger: GNN based error correction in De Bruijn Graphs

GnnDebugger uses Graph Neural Networks to correct errors in De Bruijn Graphs, improving genome assembly accuracy for diploid genomes with low coverage.

Pillbox: A Leakage-Aware Foundation-Model Predictor and Lineage-Ceiling Diagnostic for Cancer Drug Response

Pillbox predicts cancer drug response using foundation models and evaluates lineage ceiling through residual correlation analysis.

Relational biological structure improves fine-mapping of causal GWAS variants under weak signal

This paper introduces GAFM and HBP methods to improve GWAS fine-mapping by leveraging biological structure, achieving higher accuracy and speed across multiple species and datasets.

SMLMFlow: Improving Structural Resolution in Single Molecule Localization Microscopy with Flow Matching

SMLMFlow enhances structural resolution in single molecule localization microscopy by combining graph neural networks and hierarchical transformers with flow matching.

Integrating Earth Observation and Graph Theory to Evaluate Urban Green Spaces Connectivity Across European Capitals

This study evaluates urban green spaces connectivity in 28 European capitals using Earth observation and graph theory, finding that network metrics better capture functional connectivity than traditional metrics.

Structure-Function Dynamics in Healthy Cognitive Aging: A Graph Signal Processing Approach

This study uses graph signal processing to analyze structural and functional brain connectivity in healthy aging, revealing distinct age-related patterns in cognitive systems.

AI-enabled discovery of small molecules targeting complementary pathways for hair follicle rejuvenation

This study uses AI to discover small molecules that rejuvenate hair follicles by targeting complementary pathways, showing increased cell viability and suppression of follicular miniaturization.

Deciphering cross-omics complexity of tissues via diagonal integration of unpaired spatial multi-omics data

This paper introduces STAMO, a spatially aware integration method for unpaired spatial multi-omics data, showing improved alignment and domain detection compared to existing methods.

A Deep Hypergraph Learning Model for Predicting Antimicrobial Combination Effects Across Bacterial Targets

This paper introduces a hypergraph neural network to predict antimicrobial combination effects across bacterial targets, achieving high accuracy and F1-scores in classifying synergy, antagonism, and non-interaction.

OMNI: Optimized Multi-view Network Integration with Heterogeneous Graph Attention for Biomedical Interaction Prediction

The paper introduces OMNI, a graph neural network framework for biomedical interaction prediction, achieving high accuracy in chemical-gene interaction prediction.

Context-Aware Hydrophobicity Modeling: HydroMap and FastHydroMap

This paper introduces HydroMap and FastHydroMap models to predict hydrophobicity from water features, enabling rapid and accurate computation of dewetting free energy for proteins and surfaces.

GATHeR: Graph-based Accurate Tool for Immunoglobulin HEavy- and Light-chain Reconstruction

GATHeR reconstructs full-length, paired B-cell receptor sequences from scRNA-seq data, improving subclass, allele, and isoform resolution, especially in naive and memory B cells.

APOSM: Pairwise preference learning improves generative small-molecule design

APOSM improves generative small-molecule design by using pairwise preference learning to enhance surrogate model reliability and active candidate selection.

Multi-level, multi-body atomic interaction graphs for machine learning-based prediction of protein-ligand binding energies

This paper introduces a graph-based scoring function for predicting protein-ligand binding energies, achieving high accuracy across multiple datasets.

Chronic Stress Exacerbates Long-term Microvascular Network Dysfunction Following Brain Trauma

This study shows that chronic stress worsens microvascular network dysfunction after brain trauma, reducing capillary coordination and vascular reactivity.

Stereochemistry-Aware Drug-Target Affinity Prediction

This paper introduces a stereochemistry-aware framework for drug-target affinity prediction, showing improved accuracy in distinguishing enantiomer affinities.

Structure-aware protein function prediction at isoform resolution

This study develops a framework for predicting protein function and domain annotation at isoform resolution using structural and sequence data, showing strong performance in benchmark tests and capturing domain changes across isoforms.

CytoGem-XAI:A Hypergraph Neural Network Framework for Genome-Scale Metabolic Modeling and Interpretable Analysis

CytoGem-XAI combines hypergraph neural networks with interpretable analysis to improve metabolic modeling and prediction in E. coli, outperforming existing methods and revealing new essential nutrients.

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

This paper introduces a dual-channel fusion graph convolutional network to reconstruct gene regulatory networks and identify key regulators from single-cell RNA sequencing data, showing improved accuracy and robustness.

Cell type-centric interaction networks define spatial architecture of intrahepatic cholangiocarcinoma

This study defines the spatial architecture of intrahepatic cholangiocarcinoma using single-cell spatial proteomics and a graph-based deep learning framework to identify multicellular spatial patterns and their association with patient outcomes.

Predicting P-glycoprotein Substrate Status Using a Pretrained Graph Neural Network: A TDC Benchmark Study

This study evaluates a pretrained graph neural network for predicting P-glycoprotein substrate status using a benchmark dataset, achieving high AUROC scores.

HyperNiche: Learning Heterophilic Cellular Niches with Hypergraph Neural Networks

HyperNiche improves clustering and biological interpretability of spatial transcriptomics data by modeling higher-order cellular niches with hypergraph neural networks.

AI-Guided Structure-Aware Modeling and Thermal Proteomics Reveal Direct Demethylzeylasteral-ACLY Interaction

The paper uses AI and thermal proteomics to identify a natural product's direct target and shows its therapeutic effect in a psoriasis model.

Bayesian optimisation and graph-based rheology enable sequence-dependent modelling of DNA materials

This paper uses Bayesian optimisation and graph-based rheology to model DNA materials by linking sequence design to material properties.

Information-Theoretic Functional Connectivity Characterizes Multiscale Network Reorganization in Postoperative Cognitive Decline

This study identifies network reorganization patterns in postoperative cognitive decline using static and dynamic connectivity measures from resting-state fMRI.

SSPSPredictor: A Sequence and Structure based Deep Learning Model for Predicting Phase-Separating Proteins

SSPSPredictor predicts phase-separating proteins using sequence and structure data, showing higher LLPS propensity in intrinsically disordered proteins and pathogenic variants.

Just Add Structure: Protein Language Models Combined with Structural Equivariance Excel at Protein Tasks

This paper introduces ProtEGNN, a model combining protein language models with geometric equivariance to improve protein property and function prediction with fewer parameters than fine-tuned PLMs.

Whole slide image analysis of the endometrial decidual reaction reveals multiscale perturbations associated with miscarriage

The paper develops a deep learning tool to analyze endometrial histology and identifies morphological correlates of miscarriage risk.

GraphTME: a framework for predicting response to immune checkpoint inhibitors by interpreting cell-cell interactions in the tumour microenvironment using spatial transcriptomics of tumour tissue

GraphTME predicts immune checkpoint inhibitor response by analyzing spatial cell-cell interactions in tumor microenvironments using spatial transcriptomics data.

Learning molecular determinants of selective small-molecule partitioning across biomolecular condensates

This paper develops a graph-based model to predict small-molecule partitioning into biomolecular condensates using existing experimental data and applies it to identify chemically diverse partitioners.