technologynetworks

Multiomics Data Integration With Machine Learning: Methods, Tools, and Biological Applications

Multiomics data integration with machine learning has become the standard approach for combining genomic, transcriptomic, proteomic, and metabolomic measurements collected from the same biological ...

Teaching AI the laws of navigation: New physics guided detector catches GNSS spoofing even in never before seen attacks

While machine learning has improved detection, most models fail when confronted with attack scenarios they have never seen before, because they learn data patterns rather than the underlying physics ...
Technology Networks

Deep Learning for Variant Calling: How DeepVariant and Neural Networks Are Improving Genomic Accuracy

Deep learning variant calling has transformed genomic accuracy. Discover how DeepVariant works, outperforms classical tools, ...
IEEE

Graph Convolutional Networks With Adaptive Neighborhood Awareness

Abstract: Graph convolutional networks (GCNs) can quickly and accurately learn graph representations and have shown powerful performance in many graph learning domains. Despite their effectiveness, ...
IEEE

Classification of Neuropsychiatric Disorders via Brain-Region-Selected Graph Convolutional Network

Abstract: For the classification of patients with neuropsychiatric disorders based on rs-fMRI data, this paper proposed a Brain-Region-Selected graph convolutional network (BRS-GCN). In order to ...
GitHub

PU-GCN: Point Cloud Upsampling using Graph Convolutional Networks (CVPR21')

2026/06/02: add fixes and troubleshooting notes for open GitHub issues: safe FPS indexing during test, corrected Hausdorff calculation, more robust CGAL evaluation compilation/P2F handling, HDF5 ...