Nature

Graph neural networks for networked analysis of gestational diabetes risk factors: a multi method framework

We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors ...
Nature

A scalable approach to investigating sequence-to-function predictions from personal genomes

Sequence-to-function (S2F) models can evaluate arbitrary DNA sequences, yet they struggle to fully capture inter-individual variation in gene expression. We introduce SAGE-net, a scalable framework ...
Investopedia

Understanding Value Engineering: Techniques to Enhance Project Efficiency

Investopedia contributors come from a range of backgrounds, and over 25 years there have been thousands of expert writers and editors who have contributed. Suzanne is a content marketer, writer, and ...
IEEE

SKIP: A Prototype-Based Scalable Knowledge Graph Representation Learning Method

Abstract: The field of knowledge graph representation learning (KGRL) has been rapidly expanding. To effectively apply KGRL models to large real-world knowledge graphs (KGs), anchor-based methods have ...
IEEE

Multimodal Fault Diagnosis Method Based on Node-Level Adaptive Attention and Dynamic Graph Learning

Abstract: To address the challenges of acoustic and vibration signals being susceptible to noise interference, coarse modality fusion, and static graph structures in industrial fault diagnosis, we ...