graph neural networks

Absence of Mathematical Content in Cuneiform Translation Sources — E8 Intelligence Research

FINDING: Search results are dominated by low-quality YouTube clickbait and one arXiv paper on ML classification of cuneiform metadata; no new mathematical tablet translation is substantiated. | MATH: No equations, constants, or ratios are extractable from the provided sources. The arXiv paper (2603.03892v2) concerns ne...

Artificial Intelligence and Computational Medicine

This chapter provides a hands-on, code-driven introduction to artificial intelligence in computational medicine, bridging the gap between theoretical understanding and practical implementation. Covering three interconnected domains (medical imaging, multimodal data integration, and AI-assisted computing), we demonstrat...

Classical DFT energies and equilibrium configurations obtained by GNN-driven Monte Carlo simulations for solvent-free polymer-grafted nanoparticles

This deposit contains the configuration–energy datasets and equilibrium configurations supporting the manuscript "Probing the many-body energy landscape of a soft glass with graph neural networks." We train an equivariant graph neural network (NequIP) on classical density functional theory energies of solvent-free poly...

Open Science for Molecular Modelling with the Open Force Field Initiative

Drawing on computational methods that are based around training to extensive condensed phase physical property and quantum mechanical datasets, I will describe some of our efforts to design accurate and transferable inter- and intra-molecular potentials, with a view to applications in condensed phase atomistic modellin...

Integrating Danger Theory into Graph Neural Networks for Early Warning Fake News Detection

The rapid propagation of fake news on social media poses a significant threat to society, demanding detection methods that are not only accurate but also timely. While Graph Neural Networks (GNNs) are powerful tools for modeling propagation cascades, they often struggle in early detection scenarios where structural inf...

Classical DFT energies and equilibrium configurations obtained by GNN-driven Monte Carlo simulations for solvent-free polymer-grafted nanoparticles

This deposit contains the configuration–energy datasets and equilibrium configurations supporting the manuscript "Probing the many-body energy landscape of a soft glass with graph neural networks." We train an equivariant graph neural network (NequIP) on classical density functional theory energies of solvent-free poly...

AI-Powered Virtual Cell

The convergence of artificial intelligence (AI) and virtual cell technology represents a transformative paradigm in computational biology, fundamentally reshaping how we model, understand, and predict cellular behavior. This comprehensive review examines the revolutionary integration of deep learning, machine learning,...

Source code for "Pseudo-label supervised graph neural network unveils robust, fine-grained, interpretable spatial domains"

Deep learning-based framework for efficient multigoal shortest path planning in indoor environments

This work proposes a deep learning-based search-space reduction pipeline (SRP) that integrates a module called MazeNet to solve indoor navigation tasks with fast runtimes while maintaining accuracy, and evaluates MazeNet’s runtime and path-length performance using a variety of planning methods against both exact and ap...

Absence of Mathematical Content in Cuneiform Translation Sources — E8 Intelligence Research

FINDING: Search results are dominated by low-quality YouTube clickbait and one arXiv paper on ML classification of cuneiform metadata; no new mathematical tablet translation is substantiated. | MATH: No equations, constants, or ratios are extractable from the provided sources. The arXiv paper (2603.03892v2) concerns ne...

A Graph-based Approach to Predicting Protein-Protein Interactions

Accurate identification of protein-protein interactions (PPIs) is fundamental for understanding cellular mechanisms and facilitating drug discovery. Although high-throughput experimental methods have expanded the known interactome, they remain resourceintensive and prone to noise. Consequently, computational approaches...

Graph Neural Networks for Morphometric Characterization of the Aortic Valve