HackerNoon

The Race to Build AI’s Context Layer Is Really About Meaning

Context graphs, graph memory, and ontologies for AI are converging. What does this mean for enterprise AI in 2026?
News-Medical.Net

New AI framework outperforms conventional biomarkers in predicting cancer survival

Digestive system cancers, including hepatobiliary and gastrointestinal malignancies, remain a major global oncological burden ...

AI deciphers long-range DNA signals behind RNA splicing

Accurate RNA splicing is essential for gene expression and human health, yet predicting how DNA sequence variations affect ...

Cochlea network model reveals how inner ear may sort sound from noise

Over 70 million people in the U.S. are impacted by hearing loss, and age-related hearing loss is the second most common ...
AlphaGalileo

AdapGNN: A New Framework Enhances Explainability of Graph Neural Networks for Molecular Property Prediction

Researchers have developed AdapGNN, a novel model-agnostic framework that addresses the oversmoothing problem in graph neural ...
IEEE

Multigranularity Deep Graph Convolutional Neural Network Node Clustering Leveraging Spatial Information

Abstract: In the era of information explosion, clustering analysis of graph-structured data and empty graph-structured data is of great significance for extracting the intrinsic value of data. From ...
AlphaGalileo

Breakthrough in Efficient GNN Training Architecture: Research Team Proposes Hardware-Algorithm Co-Optimization Solution

A Chinese research team has achieved a breakthrough in improving the training efficiency of Graph Neural Networks (GNNs). They introduced an ...
GitHub

Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering

The code in this repository implements an efficient generalization of the popular Convolutional Neural Networks (CNNs) to arbitrary graphs, presented in our paper: Michaël Defferrard, Xavier Bresson, ...
Frontiers

DEG-BRIN-GCN: interpretable graph convolutional framework with differentially expressed genes brain region interaction network prior for AD diagnosis

Due to the intricate dynamic coupling between molecular networks and brain regions, early diagnosis and pathological mechanism analysis of Alzheimer's disease (AD) remain highly challenging. To ...