graph neural network

This paper introduces HV-MultiScaleGNN, a graph neural network framework for predicting host-virus protein-protein interactions using network topology, achieving strong performance across multiple evaluation metrics.

The paper introduces a model to classify brain dynamics based on chronnectome complexity, showing high accuracy in distinguishing cognitive states and age-related changes.

JANUS discovers somatic structural variants in cancer genomes using a pangenome graph approach, achieving high accuracy and efficiency compared to traditional methods.

This paper introduces HHRM, a hierarchical model for tumor subtype classification, achieving high accuracy in pan-cancer and breast cancer subtyping with interpretable biomarker identification.

This paper introduces dreems, a graph neural network that efficiently estimates effective migration surfaces from genetic data, achieving lower error and faster computation than existing methods.

This paper introduces CircuitGNN, a graph neural network that infers neural connectivity from population activity, showing high accuracy and robustness across different circuit sizes and recording conditions.

FateDriver prioritizes candidate regulators for specific cellular transitions using a weakly supervised dual-view graph neural network, outperforming existing methods in recovering known regulators.

The paper uses graph neural networks to interpret microbial association patterns in IBD, showing that coordinated associations, not just abundance shifts, are predictive.

This paper uses a graph neural network to predict antibacterial activity against Acinetobacter baumannii and identifies piperazine as a key contributor to predicted activity, leading to the testing of CE3F4.

This paper introduces a protein conformation prediction model optimized for structural accuracy using a structure-aware GNN, achieving results comparable to sequence-focused methods without sequence recovery supervision.

The paper develops a multi-layered GCN to identify TME-guided PDAC subtypes, revealing seven subtypes with distinct molecular and prognostic features.

The paper identifies a shared topological organisation in divergent arenavirus intergenic RNAs using graph theory and structural analysis.

The paper introduces the Core Coefficient (CC) to quantify transcription factor functional dominance in regulatory networks, showing it can predict therapeutic target shifts in disease progression.

This paper introduces SPOT-Mut, a graph convolutional neural network that predicts clonal mutations from spatial transcriptomics data, achieving high precision and recall for key driver genes.

This paper introduces an interactive server that combines a structure-aware GNN and ESMFold for bidirectional protein design, enabling users to analyze and validate design hypotheses without software installation.

The paper introduces SPInfer, a workflow for spatial proteomic inference that improves cell segmentation in human brain tissue, enabling better biological discovery at population scale.

This study identifies phenotype-cloud transitions as an organizational feature of cancer progression and therapeutic adaptation, showing how tumours reorganize phenotypic states during progression and treatment.

geoGraph models organism movement and connectivity across geographic space using hexagonal grids, improving accuracy over rectangular grids by avoiding distortion.

The paper introduces β-Stability, a graph-based framework for semi-automated curation of animal identities from images, achieving high accuracy with limited expert review.

This paper introduces RA-GHN, a framework for antibody sequence-structure co-design that captures multi-body interactions and structural constraints, outperforming existing methods.

LigRNA predicts RNA-ligand binding sites using a graph neural network that combines language model embeddings and physicochemical features, achieving improved performance over existing methods.

NoiTox presents a multi-model approach for peptide toxicity prediction, revealing label noise in datasets and showing that physicochemical descriptors are informative.

This paper introduces edge-aware graph attention networks to interpret biophysical mechanisms from molecular dynamics simulations, showing improved prediction and communication network analysis.

Adding Boltz2 distogram edges and features to protein and RNA graph networks beats MD-correlation graphs on binding tasks and improves stability, interaction and RNA predictions without simulation.

OmniScore learns shared geometric representations of diverse biomolecular complexes and improves quality assessment and interaction prediction across molecular classes.

This paper introduces HARMONIC, a framework that improves cell-cell communication inference in spatial transcriptomics by incorporating histology data, showing it better captures anatomical context.

This paper introduces zPolaron, a graph neural network for functional protein design that integrates structural, interaction, and evolutionary information, improving fitness prediction beyond structural compatibility.

This paper develops an AI-driven vaccine design framework for Mpox, yielding three multi-epitope constructs validated for structural stability, immune binding, and expression efficiency.

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