graph neural networks

Measuring AI Progress in Drug Discovery: A Reproducible Leaderboard for the Tox21 Challenge

Abstract Deep learning has fundamentally transformed fields such as computer vision and natural language processing since the early 2010s and has strongly influenced biomedical research. For drug discovery specifically, a key inflection point─akin to vision’s “ImageNet moment”─arrived in 2015, when deep neural networks...

A Multi-Resource Coordinated Scheduling Optimization Framework Integrating Berth Allocation, Quay Crane Operation, and AGV Transportation in Automated Container Terminals

This study investigates the operations in automated container terminals, in which scheduling decisions need to be generated automatically. Therefore, an integrated optimization model is established considering the two-way transportation process of containers between the berth and the yard with the assignment and schedu...

RoBCtrl: Attacking GNN-Based Social Bot Detectors via Reinforced Manipulation of Bots Control Interaction

Social networks have become a crucial source of real-time information for individuals. The influence of social bots within these platforms has garnered considerable attention from researchers, leading to the development of numerous detection technologies. However, the vulnerability and robustness of these detection met...

$$\varDelta $$-Net: Computing Tree Decomposition Using Monte Carlo Tree Search and Graph Neural Network

Multimodal Artificial Intelligence in Lung Cancer: From Data Integration to Precision Oncology

Current AI applications in lung cancer related to radiologic imaging, digital pathology, genomics, immunohistochemistry, and Cell Painting morphology are examined and their potential to be incorporated into a proposed multimodal model is examined.

Revealing Seasonal Environmental Associations and Spatial Heterogeneity of Pacific Yellowfin Tuna CPUE Using an Interpretable Neural Network Framework

Understanding the spatial distribution patterns of pelagic species such as yellowfin tuna (Thunnus albacares) is essential for ecosystem-based fisheries management. However, characterizing CPUE–environment relationships remain challenging because these relationships may be nonlinear and spatially heterogeneous across l...

Feature-Personalized Graph Contrastive Clustering for ScRNA-Seq Data

Graph Neural Network and Transformer Fusion for Molecular Property Prediction: What a Strict Evaluation Protocol Finds

An evaluation protocol for multi-view molecular property prediction, and a fusion architecture measured under it. Eight MoleculeNet datasets, six scaffold splits (the DeepChem canonical split plus five seeded), paired comparisons with Holm correction and an across-dataset test, split-conformal uncertainty (LAC, APS, RA...

Graph Neural Network and Transformer Fusion for Molecular Property Prediction: What a Strict Evaluation Protocol Finds

An evaluation protocol for multi-view molecular property prediction, and a fusion architecture measured under it. Eight MoleculeNet datasets, six scaffold splits (the DeepChem canonical split plus five seeded), paired comparisons with Holm correction and an across-dataset test, split-conformal uncertainty (LAC, APS, RA...

Integrating Molecular Semantics and Three-Dimensional Geometry for Critical Property Prediction: A Multimodal GNN-BERT Framework

Accurate prediction of critical temperature, pressure, and volume is essential for thermodynamic modeling and process safety design, yet remains challenging for complex molecules under data-scarce conditions. Here, we develop a multimodal GNN-BERT framework that integrates SMILES-based chemical semantics with two-dimen...

Adversarial Robustness of Complex-Valued Graph Attention Beamformers: A Gradient Efficiency Study

Graph neural network (GNN) based beamformers have demonstrated near-optimal performance for multi-user MISO systems, yet their security under adversarial channel perturbations remains unexplored. We study the adversarial robustness of a complex-valued Graph Attention Network (GAT) beamformer trained via unsupervised La...

Spectral-temporal graph neural networks for demand forecasting on bipartite supply-customer graphs

Diese Masterarbeit untersucht, ob eine explizite Kunden–Produkt-Graphstruktur die monatliche industrielle Nachfrageprognose verbessern kann. Dafür wird ein TGGC-ähnliches spektral–temporales Graph Neural Network auf zwei Graphformulierungen angewendet: einen festen bipartiten Kunden–Site–Produkt-Graphen sowie einen lat...