graph neural networks

Datasets, Trained Graph Neural Network Models, and Source Code for Protein Binding Site Residue Prediction

This record contains the datasets, trained graph neural network models, and source code supporting the manuscript “Enhancing protein binding site residue prediction with graph neural networks: impacts of cutoff distance and feature selection.”

Antibody-drug conjugate engineering: from design to efficacy and safety

Antibody–drug conjugates (ADCs) represent a rapidly expanding class of targeted cancer therapeutics that combine the high selectivity of monoclonal antibodies with the potent cytotoxic activity of small-molecule drugs. Their clinical success relies on the simultaneous optimization of multiple interdependent parameters,...

Code and frozen results for: Auditing Site-Dependent Performance in Transductive Population Graph Neural Networks for Multisite Autism fMRI

Reproducibility software and frozen analysis artifacts for the study "Auditing Site-Dependent Performance in Transductive Population Graph Neural Networks for Multisite Autism fMRI". The release includes the deterministic evaluation harness, recovered per-seed outer-fold generation partitions, the reconstructed mean-fo...

OISES-Graph-Surrogate: geometry-aware surrogate modelling and inverse design of origami-inspired super-expandable scaffolds

Virtual laboratory and graph neural surrogate for origami-inspired super-expandable scaffolds in distraction osteogenesis: co-rotational beam and pore-scale Stokes solvers, an STL-to-graph front end, a multi-task heteroscedastic Scaffold Graph Network, parametric baselines, and the optimisation, ablation and repetition...

source code for scale inference models

Graph Neural Network Models for Scale Inference of Linear Features with Integrated Geometric and Topological Features

Constraint-Guided LLM-GNN Framework for Anomalous Journal Entry Detection in Auditing

Journal Entry Testing (JET) is a fundamental audit procedure to identify any potential misstatements, fraud, and management override of controls. Traditional rule-based JET methods suffer from high false positive rates and limited ability to detect complex anomaly patterns. Recent work has shown that large language mod...

Coherence-Rate Graphs and the Trainability of Quantum Neural Networks Under Noise

Rapid prediction of full-field stress evolution during drilling of carbon fiber reinforced polymer: A finite element database-driven graph recurrent surrogate model

Stress evolution during carbon fiber reinforced polymer (CFRP) drilling governs drilling-induced damage and strongly affects hole quality and the structural reliability of aerospace components. Rapid and accurate prediction of stress-field evolution is essential for online process optimization and damage control. Exper...

Code and frozen results for: Auditing Site-Dependent Performance in Transductive Population Graph Neural Networks for Multisite Autism fMRI

Reproducibility software and frozen analysis artifacts for the study "Auditing Site-Dependent Performance in Transductive Population Graph Neural Networks for Multisite Autism fMRI". The release includes the deterministic evaluation harness, recovered per-seed outer-fold generation partitions, the reconstructed mean-fo...

Pulmonary Embolism Risk Stratification from CTPA and Medical Records: Vascular Graphs Are Not All You Need

Personalized single-cell transcriptomics reveals molecular diversity in Alzheimer’s disease

Alzheimer’s disease (AD) is highly heterogeneous and driven by diverse molecular and cellular mechanisms. Functional genomics investigates these mechanisms from genetic variants to gene expression and regulation. We performed personalized functional genomics analysis on population-scale single-nucleus RNA-seq data, wit...

AI-based characterization of Alzheimer's disease phenotypes from population-scale single-cell data.

The complexity of Alzheimer's disease (AD) manifests in diverse clinical phenotypes, including cognitive impairment and neuropsychiatric symptoms. However, the etiology of these phenotypes remains elusive. To address this, the PsychAD project generated a population-level single-nucleus RNA sequencing dataset comprising...