graph neural network

Teleport-Stabilized Quantum-Walk Ranking in Near-Tie Neoantigen Regimes

The paper introduces a quantum-walk-based ranking method to stabilize neoantigen selection in near-tie scenarios by leveraging graph structure and coherence dynamics.

Multi-Modal Deep Learning Integrates Spatial Topologies and Sequential Motifs to Identify Class I HDAC Inhibitors as Pan-Cancer Therapeutics

This paper develops a multi-modal deep learning framework to identify Class I HDAC inhibitors as broad-spectrum anti-cancer agents by integrating chemical and transcriptomic data.

Machine Learning-Guided Synthetic Microbial Communities Enable Functional and Sustainable Degradation of Persistent Environmental Pollutants

The paper develops GENIA, a machine learning framework for designing synthetic microbial communities that degrade multiple persistent pollutants more effectively than existing methods.

Mosaic integration of spatial multi-omics with SpaMosaic

SpaMosaic integrates spatial multi-omics data using contrastive learning and graph neural networks, improving spatial domain identification and missing modality imputation across diverse datasets.

Graph Neural Networks (GNNs) for Protein-Ligand Interaction Prediction

This paper explores the use of Graph Neural Networks for predicting protein-ligand interactions, focusing on improving interpretability and accuracy through various techniques.

Geometry aware graph attention networks to explain single-cell chromatin state and gene expression

SEAGALL uses geometry-aware graph attention networks to extract biologically relevant features from single-cell data, revealing cell identity drivers beyond marker genes.

EpiRanha: Hunting for Epitope Similarity with a Structure- and Residue-Aware Graph Neural Network

EpiRanha identifies epitope similarities using a structure- and residue-aware graph neural network, improving detection of cross-reactive epitopes and off-target interactions.

Proximity-Aware Graph Attention Networks for Spatially Resolved Cell-Cell Inference

This paper introduces a proximity-aware graph attention network to infer cell-cell communication from spatial transcriptomics data, achieving improved performance over existing methods.

A Hierarchical Spatial Graph Neural Network Resolves Immunogenic and Tolerogenic Tertiary Lymphoid Structures in Renal Cell Carcinoma

This paper introduces a hierarchical spatial graph neural network to classify tertiary lymphoid structures as immunogenic or tolerogenic in renal cell carcinoma using spatial transcriptomics data.

Concordia: Spatial Domain Detection via Augmented Graphs for Population-Level Spatial Proteomics

Concordia detects spatial domains in population-level spatial proteomics data using a GNN-based framework, revealing a cancer-associated fibroblast subset linked to clinical outcomes in lung cancer.

Structure-aware graph attention based hierarchical transformer framework for drug-target binding affinity prediction

This paper introduces a graph transformer model for drug-target binding affinity prediction, showing improved performance on benchmark datasets.

Systematic Benchmarking of Kinase Bioactivity Models Across Splitting Strategies and Protein Representations

This study benchmarks kinase bioactivity models across splitting strategies and protein representations, finding that Random Forest with Morgan fingerprints outperforms neural models in scaffold and target-based evaluations.

Structure-informed Siamese graph neural networks classify CirA missense variants with implications for cefiderocol susceptibility

The paper develops a structure-informed Siamese GNN to classify CirA missense variants affecting cefiderocol susceptibility using synthetic data.

Full-Atom MPNN Based Redesign of Plant Dehydrogenase Enables Thermostability Enhancement Without Loss of Stereoselectivity

The paper uses FAMPNN to redesign SrBDH1 for increased thermostability while maintaining stereoselectivity.

Modular Deep Learning for Direct RNA Sequence Design via Self-Contained RNA Units

The paper introduces SCRU-DB, a database of RNA units, and two models, SCRU-Seq and SCRU-Diff, to design RNA sequences with high structural accuracy.

GraphPop: graph-native computation decouples population genomics complexity from sample count

GraphPop reduces population genomics complexity by using graph databases, enabling faster analysis of large datasets and revealing population structure in rice and humans.

GraphMana: graph-native data management for population genomics projects

GraphMana manages population genomics data using a graph database, enabling efficient incremental updates and export to multiple formats.

An Explainable Knowledge Graph-Driven Approach to Decipher the Link Between Brain Disorders and the Gut Microbiome

This paper introduces GNN-GBA, a graph neural network that identifies mechanistic pathways linking gut microbes to brain disorders using a large biomedical knowledge graph.

A deep learning predictor of bindable protein surfaces toguide generative synthetic biology

This paper introduces IARA, a deep learning model that rapidly identifies optimal binding interfaces for protein binder design, reducing computational costs.

KIMMDY: a biomolecular reaction emulator

KIMMDY emulates biomolecular reactions using kinetic Monte Carlo and graph neural networks, validated against experimental data and applied to various reaction types.

Interpretable Antibody-Antigen Structural Interface Prediction via Adaptive Graph Learning and Cyclic Transfer

VASCIF predicts antibody-antigen interfaces using a structure-aware graph model, achieving state-of-the-art performance and revealing biophysically meaningful interaction patterns.

DyGraphTrans: A temporal graph representation learning framework for modeling disease progression from Electronic Health Records

DyGraphTrans models disease progression from EHRs using a dynamic graph framework, achieving strong predictive performance and interpretability.

Structure-aware geometric graph learning for modeling protease-substrate specificity at scale

OmniCleave models protease-substrate specificity using a geometric graph framework, outperforming existing methods and identifying novel substrates.

Multi-Stage Graph Attention Networks for Interpretable Alzheimer's Disease Classification from Genome-Wide Association Data

This paper develops a multi-stage graph attention network to improve Alzheimer's disease classification from genome-wide association data by capturing epistatic interactions and enhancing interpretability.

Molecular Contrastive Learning with Graph Attention Network (MoCL-GAT) for Enhanced Molecular Representation

The paper introduces MoCL-GAT, a self-supervised learning framework for molecular representation, achieving state-of-the-art performance on molecular property prediction tasks.

Graph transformer for ancient ancestry inference

ARGMix uses a graph transformer to infer local ancestry from ancient DNA, showing continuity between Otzi the Iceman and present-day Europeans.

Sequence-Driven Drug-Target Affinity Prediction Via Graph Attention Networks and Bidirectional Cross-Attention Fusion

XAttn-DTA predicts drug-target affinity using graph attention networks and cross-attention fusion, achieving improved performance on benchmark datasets.

CLIMBS: assessing carbohydrate-protein interactions through a graph neural network classifier using synthetic negative data

CLIMBS is a graph neural network classifier that assesses carbohydrate-protein interactions using synthetic negative data, showing high accuracy and specificity.

GATSBI: Improving context-aware protein embeddingsthrough biologically motivated data splits

GATSBI improves protein embeddings by using biologically relevant data splits, showing better performance for understudied proteins in various tasks.

GraphBG: Fast Bayesian Domain Detection via Spectral Graph Convolutions for Multi-slice and Multi-modal Spatial Transcriptomics

GraphBG detects spatial domains in multi-slice and multi-modal spatial transcriptomics data using spectral graph convolutions and Bayesian modeling, achieving high accuracy and scalability.