graph neural network

Ergodic breakdown of brain dynamics under chronnectome complexity across cognitive states and age

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

Somatic structural variation discovery on cancer pangenome graphs

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

Classifying tumors from broad category to hierarchical molecular subtype: an interpretable hierarchical framework for precision oncology

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.

Deep Recovery of Effective Migration Surfaces

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.

Graph neural network approach for inferring neuronal connectivity

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.

Overcoming segmentation bottlenecks in brain tissue for spatial proteomic inference at population scale

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.

Relation-Aware Graph-Hypergraph Networks for Antibody Sequence-Structure Co-Design

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

qcGEM: a graph-based molecular representation with quantum chemistry awareness

qcGEM introduces a quantum-chemistry-aware graph-based molecular representation that improves performance and interpretability in molecule-related tasks.

NoiTox: A Machine Learning Strategy for Peptide Toxicity Prediction Reveals Label Noise in Benchmark Datasets

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

Learning Dynamic Protein Representations at Scale with Distograms

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: Universal Scoring of Diverse Biomolecular Complexes via Equivariant Geometry-Aware Discrete Representation Learning

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

AI-Driven Design of Next-Generation Immunoinformatics Multi-Epitope Subunit Vaccine Targeting the Most Virulent Mpox Proteome: A Structural and Kinetic Validation Framework

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.

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.

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.

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).

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.

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.

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.

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.

Pep-PU-GAN: Positive-Unlabeled Adversarial Learning for Peptide Function Prediction

Pep-PU-GAN improves peptide function prediction using PU learning and GANs with graph neural networks, achieving high F1 and AUROC scores on neuropeptide classification.

TLS-Sim: An Open-Source Toolkit for Individualized Transcranial Light Stimulation Modeling with Deep-Learning-Accelerated Simulation

TLS-Sim provides a toolkit for individualized transcranial light stimulation modeling using deep-learning-accelerated Monte Carlo simulations to improve efficiency and accuracy.

ProMaya: a hierarchical universal Deep Learning framework for accurate and interpretable Protein-Protein interaction identification

ProMaya identifies protein-protein interactions using a hierarchical graph-transformer framework, achieving >95% accuracy across nine species and outperforming existing tools by >12%.

Generalizable Cysteine Quantification in Pea Cultivars from SERS Spectra Using AI

The paper uses AI to quantify cysteine in pea cultivars from SERS spectra, finding that a 1D-CNN outperforms other models in accuracy and generalization.

GIN-CRC-Pareto: A graph-based Pareto-optimal multi-task learning framework to identify miRNA-target interactions in colorectal cancer

GIN-CRC-Pareto identifies miRNA-target interactions in colorectal cancer using a graph-based multi-task learning framework, outperforming existing methods in accuracy and AUC.

Comprehensive Evaluation of Protein Language Model Embeddings for Drug-Target Affinity Prediction

This study evaluates the impact of pre-trained protein language models on drug-target affinity prediction, showing that simple architectural changes can match large PLMs' performance.

TomatoPGFM: A graph-conditioned foundation model for tomato pangenomes

TomatoPGFM is a graph-conditioned foundation model for tomato pangenomes that improves sequence representation using graph context, achieving higher classification performance than DNABERT-2.

Pro-GAT: Reconnecting Fragmented PROTACs Using Graph Attention Transformer

Pro-GAT repairs chemically disconnected PROTAC candidates generated by diffusion models, improving validity while maintaining uniqueness.

A Generative Virtual Tissue Model Enables Computational Design of Therapeutic Perturbation Strategies

This paper introduces CIFM, a generative virtual tissue model that simulates transcriptional dynamics under therapeutic conditions to design immunotherapy strategies for cancer and autoimmune diseases.

ClinOracle: Hierarchical AI Prediction of Target Binding and Patient-Derived Functional Activity Across Diverse Therapeutic Targets

ClinOracle uses a hierarchical graph neural network to predict target binding and patient-derived functional activity, improving compound prioritization through a Priority Score combining translational probability and developability.