GNN, a framework to train robust GNNs under noisy conditions. Soft-GNN mitigates label noise impact through dynamic ...

Endogenous intracellular allosteric modulators of GPCRs remain largely unexplored, with limited binding and phenotype data available. This gap arises from the lack of robust computational methods for ...

Mobile apps now offer practical ways to learn data science, from coding and statistics to machine learning, anytime and ...

Contrastive learning (CL), which leverages unlabeled data for pretraining, can mitigate this issue by reducing the dependency on extensive manual annotation. To address the issues, we propose ...

In this paper, we propose an end-to-end framework for EEG classification that integrates power spectral density (PSD) and visibility graph (VG) features together with deep learning (DL ... the ...

Transformers process natural language as a sequence on finite context windows; however, global relationships among words beyond these windows cannot be completely modeled via ... to graph ...

Learn how large language models like ChatGPT make knowledge graph creation accessible, revealing hidden connections in your ...

Instead of storing data in tables consisting of rows and columns, it utilizes a graph structure made up of nodes, edges and properties, to represent and store information. It’s a more versatile ...

In a major leap forward for genetic and biomedical research, scientists have developed a powerful new artificial intelligence tool that can predict the 3D shape of chromosomes inside individual cells ...

High-entropy alloys (HEAs) offer tunable compositions and surface structures, presenting significant potential for creating novel active sites to enhance CO2 reduction (CO2RR) catalysis, a key process ...

A new study in Small introduces OptiMate, a machine learning model that predicts optical properties and identifies ...