graph neural network

GraphTox: A Semi-Supervised Pre-Trained Framework for Peptide Toxicity Prediction using Geometric Graph Transformer and LORA-Based Finetuning

GraphTox predicts peptide toxicity using a geometric graph transformer and LORA-based finetuning, outperforming existing methods by incorporating structural information.

GAE-Δ: A Graph-Learning Framework for Gene Network Rewiring and Clinical Outcome Prediction from Multi-Omics Data

GAE-Δ uses a graph autoencoder to analyze multi-omics data and predict clinical outcomes while identifying gene network rewiring associated with cancer progression.

Pathogen-specific antimicrobial activity prediction with biological large language model-based methods

The paper introduces triAMPh, a framework for predicting pathogen-specific antimicrobial peptide activity using biological large language models, showing it matches or outperforms existing methods.

Efficient and accurate near telomere-to-telomere haplotype reconstruction of diploid genomes

HapFold improves haplotype reconstruction for diploid genomes by combining graph-based and sequence-based scaffolding methods, achieving high accuracy and contiguity.

AE-PocketMiner Uses Attention to Simultaneously Predict Cryptic Pockets and Their Allosteric Coupling

AE-PocketMiner predicts cryptic protein pockets and their allosteric coupling using a graph neural network, outperforming existing methods and validating predictions experimentally.

Heterogeneity-driven adaptive scale graph learning for subcellular spatial transcriptomics

This paper introduces HAST, a framework for spatial transcriptomics that adapts graph learning scales to tissue heterogeneity, improving spatial structure identification and tumor region characterization.

DGAT: A Dual-Graph Attention Network for Inferring Spatial Protein Landscapes from Transcriptomics

DGAT imputes spatial protein expression from transcriptomics-only data, revealing cell states and tissue organization not visible from RNA alone.

Dual-channel graph learning reveals similarity and complementarity in protein-protein interaction networks

DMG-PPI is a dual-channel graph neural network that models similarity and complementarity in PPI networks, improving prediction accuracy and providing interpretable insights into binding mechanisms.

Structural Connectome Analysis using a Graph-based Deep Model for Age and Dementia Prediction

The paper predicts age and MMSE scores from brain connectivity graphs using a graph-based deep learning model, showing improved performance over existing methods.

Predicting and Elucidating Peptide Retention Mechanisms with Graph Attention Networks

The paper introduces a deep learning model to predict peptide retention times in liquid chromatography and identifies key factors influencing retention through saliency mapping.

NuGraph: Graph-Based Reasoning over 3D Primitives for Nucleus Segmentation Correction

This paper introduces a graph-based framework for correcting nucleus segmentation errors in 3D reconstructions, achieving high accuracy on brain-scale benchmarks.

Learning Chirality-Aware Representations to Predict Drug Side Effect Frequencies

This paper introduces F2S, a method that predicts drug side effect frequencies by accounting for chirality, achieving comparable performance to state-of-the-art methods with fewer false positives.

Data-modeling the interplay between single cell shape, single cell protein expression, and tissue state

The paper investigates the relationship between cell shape, protein expression, and tissue state using spatial imaging and machine learning, finding a bidirectional link and identifying p53-positive tumor cells.

DynoSys 2.0: Graph-Based Modeling of Dynamic Risk States and System Transitions in Human Behaviours Development

DynoSys 2.0 models dynamic behavioral risk states using graph-based temporal modeling with longitudinal data from the ABCD Study, showing better prediction for externalizing behaviors and substance use initiation.

Uncertainty-aware graph representation learning with positive-unlabeled classification for biomarker discovery in peripheral artery disease

This paper develops an uncertainty-aware framework for PAD biomarker discovery using graph neural networks and positive-unlabeled learning, identifying novel and validated candidate proteins.

Predicting curvature evolution on biological surfaces from clinical imaging-derived area dilation: a closed-form interpretable framework

The paper develops a closed-form equation to predict curvature changes in thoracic aortas from CT scans, showing strong performance on synthetic and clinical data.

pyTrance finds co-localizing RNAs in subcellular spatial transcriptomics data

pyTrance identifies co-localizing RNAs in spatial transcriptomics data with higher accuracy than existing methods, revealing biologically relevant RNA co-localization patterns in mouse brain tissue.

InterScale reveals multi-scale cellular interaction programs in spatial transcriptomics

InterScale models both local and global cellular interactions in spatial transcriptomics data, revealing spatially restricted differentiation programs and disease-associated tissue remodeling.

A Scalable Sign-Aware Multi-Omics Knowledge Graph Foundation Model for Mechanistic Drug Action and Clinical Response Predictions

This paper introduces a signed multi-omics knowledge graph foundation model for drug action and clinical response predictions, achieving high accuracy and efficiency.

G-SPRI: A Structure-Centric Graph Model for Comprehensive Prediction of Cancer Driver Events from Missense Mutations

G-SPRI predicts cancer driver events from missense mutations by integrating structural and mutation-specific features using a graph model.

The Computational and Neural Basis of Zero-Shot Control in Dynamic Pursuit

The paper proposes a computational framework for zero-shot control in dynamic pursuit tasks and demonstrates its implementation in a graph convolutional network, showing transferability and change-of-mind behavior, supported by neural recordings.

PEARL: Integrative multi-omics classification and omics feature discovery via deep graph learning

PEARL is a deep graph learning method for multi-omics integration that outperforms existing methods and identifies functionally important genes in Alzheimer's disease.

TopoFuseNet: Hierarchical Graph Representation Learning with Multi-Scale Topological Features for Accurate Drug Synergy Prediction

TopoFuseNet improves drug synergy prediction by integrating hierarchical molecular structures and multi-scale topological features, outperforming existing methods on benchmark datasets.

immuneKG: An Immune-Cell-Aware Knowledge Graph Framework for Target Discovery in Immune-Mediated Diseases

The paper introduces immuneKG, a knowledge graph framework for immune-mediated diseases, achieving high performance in target discovery using a graph neural network and novel features.

KG-Bench: Benchmarking Graph Neural Network Algorithms for Drug Repurposing

KG-Bench benchmarks graph neural networks for drug repurposing using the Open Targets dataset, finding TransformerConv achieves the highest performance.

MUSE enables cross-species multi-omics integration that incorporates transcriptional regulatory modules

MUSE integrates multi-omics data across species to align cellular states while preserving species-specific features and regulatory module conservation.

PEPR-GNN: Perturbation-Enhancer-Promoter-RNA Graph Neural Networks for Multiome Perturb-Seq modeling of regulomes

The paper introduces PEPR-GNN, a graph neural network framework to model regulome responses during genetic perturbations using Multiome Perturb-Seq data.

GatorSC: Multi-Scale Cell and Gene Graphs with Mixture-of-Experts Fusion for Single-Cell Transcriptomics

GatorSC improves single-cell transcriptomic analysis by integrating multi-scale cell and gene graphs with a Mixture-of-Experts fusion framework, outperforming existing methods in clustering, imputation, and annotation.

Spartan: activation-aware framework for spatial domain and variable gene discovery

Spartan identifies spatial domains and variable genes by integrating spatial topology and activation signals in spatial transcriptomics data.

Graph-based learning and read-out of nanopore translocation event signals

This paper develops a graph-based framework for label-free detection of nanopore translocation events, showing it can predict event morphology across a range of voltages.