Machine learning workflow enables faster, more reliable organic crystal structure prediction

Prediction of crystal structures of organic molecules is a critical task in many industries, especially in pharmaceuticals ...
C&EN

Integrating ESM-2 and Graph Neural Networks with AlphaFold-2 Structures for Enhanced Protein Function Prediction

Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. Protein function prediction is essential for elucidating biological processes and ...
Forbes

Why Knowledge Graphs Are The Future Of Data Intelligence Platforms

Rajiv Shesh is the Chief Revenue Officer at HCLSoftware where he leads revenue growth & customer advocacy for Products & Platforms division. What’s really powering AI? High-quality data—foundational ...
BGR

Google AI Mode Brings Gorgeous Interactive Graphs To Search

I wasn't a big fan of Google's AI Overviews when they debuted atop Google Search. I still don't like the feature, even though it's more reliable than it was early on and, supposedly, quite popular ...
E&E

Group claiming to represent data centers objects to Fla. utility rate hike push

The Florida Energy for Innovation Association was incorporated May 7, just eight days before it asked the Public Service Commission to allow it to intervene in the rate case. TALLAHASSEE, Florida — A ...
Frontiers

stGuide advances label transfer in spatial transcriptomics through attention-based supervised graph representation learning

The growing availability of spatial transcriptomics data offers key resources for annotating query datasets using reference datasets. However, batch effects, unbalanced reference annotations, and ...
IEEE

SimE: A Knowledge Graph Embedding Model to Encode Self-Similar Structures Through Algebraic and Geometric Transformations

Abstract: Knowledge Graphs (KGs), with their intricate hierarchies and semantic relationships, present unique challenges for graph representation learning, necessitating tailored approaches to ...
IEEE

Anomaly Detection Over Multi-Relational Graphs Using Graph Structure Learning and Multi-Scale Meta-Path Graph Aggregation

Abstract: Graph Neural Networks (GNNs) have recently achieved remarkable success in various learning tasks involving graph-structured data. However, their application to multi-relational graph anomaly ...
EurekAlert!

Exploring & exploiting high-order graph structure for sparse knowledge graph completion

High sparse Knowledge Graph is a key challenge to solve the Knowledge Graph Completion task. Due to the sparsity of the KGs, there are not enough first-order neighbors to learn the features of ...
Scientific Research Publishing

A Maritime Document Knowledge Graph Construction Method Based on Conceptual Proximity Relations ()

1 COSCO Shipping Technology Co., Ltd., Shanghai, China. 2 COSCO Shipping Specialized Carriers Co., Ltd., Guangzhou, China. The cost and strict input format requirements of GraphRAG make it less ...