graph neural networks

Artificial Intelligence in Healthcare and Drug Discovery: From Predictive Diagnostics to Precision Medicine

The integration of artificial intelligence (AI) and deep learning with the biomedical research marks a critical inflection point, shifting the paradigm from addressing symptoms to predicting and acting at the molecular level. In many diagnostic models, the chain reaction triggered by a chronic infection is often overlo...

Innovative Applications of Artificial Intelligence in Bacteriophage Research: A New Chapter in Future Medicine

As the crisis of antibiotic resistance escalates, phage therapy has regained attention as an alternative strategy. Artificial intelligence (AI) technologies offer new avenues to overcome the bottlenecks inherent in traditional bacteriophage research. This review summarizes the multi-dimensional innovative applications...

Disentangling Loss Design and Ontology-Aware Architecture in GNN-Based Protein Function Prediction A Controlled Ablation Study

Artificial Intelligence–Driven Digital Twins, Multi-Omics and Precision Hepatitis Care: Emerging Innovations, Challenges, and Future Perspectives

Due to persistent diagnostic delays, varied disease progression, and unpredictable therapy responses, viral hepatitis remains a major worldwide health concern despite advances in antiviral medication and vaccine. Precision hepatology is a viable strategy for overcoming these limitations through tailored, data-driven cl...

A neural-symbolic dynamic graph framework for real-time anomaly detection in software-defined industrial cyber-physical systems

Software-defined industrial cyber-physical systems (SD-ICPS) face emerging security challenges as sensor measurements, system logs, and network traffic become increasingly interconnected under stringent timing requirements. Most existing anomaly detection methods rely on a single modality, provide limited modeling of c...

STLC-Net: Skin Transfer Learning Classification Framework for Multi-Class Skin Cancer Detection and Clinical Decision Support

STLC-Net (Skin Transfer Learning Classification Network) is introduced, a robust, zero-data-leakage framework for automated seven-class skin lesion classification using the benchmark HAM10000 dataset and integrated into an interactive web deployment for real-time, interpretable clinical decision support.

AI in energy and water conservation

AI-Enabled National Water Conservation and Security System for Qatar A Research and Policy Framework for an Intelligent National Water Digital Twin, Predictive AI and ILM-Based Water Intelligence Platform Prepared as a concept paper for Qatar Government consideration Abstract Qatar’s water security challenge is fundame...

IEMAP—The Italian Energy Materials Acceleration Platform—And Its Application to Cathode Materials for Batteries

The discovery of energy materials increasingly couples high-throughput computation with machine learning, but the data and models behind a given study are rarely left in a state that lets others, or the original authors months later, re-run or extend the work. IEMAP, Italian Energy Materials Acceleration Platform, was...

Three-dimensional geological modeling based on dual-task stratigraphy-aware attention networks (Geo-SAN v1.0)

The current three-dimensional (3D) geological implicit modelling methods are mainly based on interpolation methods, which struggle to capture the nonlinear characteristics of complex geological structures and are limited in their capacity to integrate multi-source modeling data. To overcome these limitations, we propos...

MPGSD-AEB

This software package contains the Model04 EIGT-Net implementation developed for the MPGSD-AEB study. The package includes the trained model parameters, PyTorch model implementation, graph definition, MATLAB/Simulink forward-inference model, and fixed-step data feeder for hardware-in-the-loop preparation. The model pro...

Comparative Evaluation of Graph Neural Networks for Molecular Lipophilicity Prediction

Predicting molecular properties is a central challenge in cheminformatics and plays a critical role in drug discovery and materials science. Lipophilicity, which describes the partitioning of a molecule between lipid and aqueous environments, is a key physicochemical property influencing drug behavior. Although quantum...

Machine Learning for Process Optimization in Waste Biomass Conversion Technologies

Biomass waste is a valuable renewable energy resource, but its efficient conversion is hindered by feedstock variability, complex non-linear reactions, and the limited adaptability of conventional control systems. This chapter examines how machine learning improves biomass conversion through intelligent process monitor...