graph neural networks

Rigorous validation of graph-based network analysis reappraises biologically coherent ADHD-associated transcriptomic modules in peripheral blood

Background Graph attention networks (GATs) are increasingly applied to transcriptomic data because they integrate gene-network structure while producing attention weights that are often interpreted as indicators of biological importance. However, whether attention-derived explanations reliably reflect biologically mean...

CALT-GNN: a graph neural network with cross-attention and long-tail experts for multi-omics cancer subtype classification

Abstract Cancer subtype classification based on molecular features is important for characterizing tumor heterogeneity, evaluating patient prognosis, and supporting precision treatment. Multi-omics integration provides complementary molecular information across genomic, epigenomic, transcriptomic, and proteomic levels....

Towards More Realistic and Practical Graph Backdoor Attacks

Graph Neural Networks (GNNs) have demonstrated remarkable performance on graph-based learning tasks and are increasingly deployed in security-critical applications. However, recent studies have shown that they are highly vulnerable to graph backdoor attacks (GBAs), where adversaries implant malicious triggers to induce...

Interval Estimation in Water Distribution Systems Using Physics-Informed Graph Neural Networks

Abstract Artificial Intelligence in general and Machine Learning (ML) in particular has the potential to play a key role in critical infrastructure such as Water Distribution Systems (WDSs). In the face of urban population growth, ML can pave the way towards smart cities by not only solving various tasks in WDSs but al...

Drugging the Undruggable in Oncology: Structural Computation, Machine Learning, and Generative AI for Historically Intractable Targets

For four decades the central oncogenic drivers of human cancer the RAS GTPases, the transcription-factor MYC, mutant p53, and the protein-tyrosine phosphatases, fusion oncoproteins and intrinsically disordered regulators that surround them were regarded as undruggable: lacking deep, well-defined small-molecule binding...

IMOS-SWIL District Heating Dataset

This dataset provides synchronized, high-resolution multi-physics measurements collected by IMOS-EPFL in collaboration with Aalborg University using the controlled district heating testbed at the Smart Water Infrastructures Laboratory (SWIL), Aalborg University, Denmark. The data were collected as part of the Intellige...

Artificial Intelligence in Photodynamic Therapy: From Molecular Design of Photosensitizers to Intelligent Delivery, Adaptive Dosimetry, and Personalized Prognostication of Results

Photodynamic therapy (PDT) is a minimally invasive therapeutic approach based on the light-induced activation of a photosensitizer in the presence of molecular oxygen, resulting in the generation of reactive oxygen species, including singlet oxygen. Despite local selectivity and low systemic toxicity, reproducibility o...

Mapping Urban Mixed Land Use via Multimodal Fusion and Large Language Models

The precise delineation of urban land use, particularly mixed land use, is essential for sustainable urban planning and resource allocation. However, traditional pixel-based mapping methods face a profound "semantic gap" and struggle to decode the complex, overlapping functional dynamics of modern cities. This study pr...

Strain-Engineered Magnetism, Super-Exchange Physics, and Thermal Phase Transitions in Monolayer Chromium Trihalides CrX3 (X = Cl, Br, I)

This repository contains the Python calculation scripts, raw data JSON files, and publication-quality vector figures supporting the manuscript: "Strain-Engineered Magnetism, Super-Exchange Physics, and Thermal Phase Transitions in Monolayer Chromium Trihalides CrX3 (X = Cl, Br, I)" ### Repository Structure & Contents:...

ATAN: A hybrid graph neural network framework for accelerating aging-aware timing analysis

CMOS device aging critically challenges digital circuit design, where conventional timing analysis struggles with the large number of input conditions, standard cells, and process corners. This work proposes ATAN, a novel framework for accelerating aging-aware timing analysis, employing a hybrid graph neural network. S...

Integrating Blockchain and AI for Secure and Scalable Financial Transaction Monitoring

The global financial system processes trillions of dollars in transactions daily, creating an urgent need for monitoring systems that are secure, scalable, privacy-preserving, and capable of detecting sophisticated fraud in real time. Despite a decade of development, existing approaches are fundamentally fragmented: ru...

UEXGNN: reference inference release of a physically constrained autoregressive graph neural network for transient-field prediction of near-seabed underwater explosions