Six additional scRNA-seq datasets are reserved for the biological case studies (Sections 3.8–3.10) and excluded from every benchmark statistic: sleep-deprivation bone marrow (GSE280145), a TPO-induced ...
Abstract: Graph self-supervised learning (GSSL) has shown great promise in addressing the label scarcity problem in real-world graphs, yet existing generative self-supervised learning (SSL) methods ...
Cloud infrastructure anomalies cause significant downtime and financial losses (estimated at $2.5 M/hour for major services). Traditional anomaly detection methods fail to capture complex dependencies ...
A Local-Scale Dataset of Annual Spatiotemporal Maps of Physical Vulnerability in the Cyclone-Impacted Coastal Khurushkul Community (Bangladesh) and Mudslide-Affected Freetown (Sierra Leone) (2016–2023 ...
Computational Biology and Molecular Simulations Laboratory, Department of Biophysics, School of Medicine, BahçeÅŸehir University, Istanbul 34349, Turkey Lab for Innovative Drugs (Lab4IND), ...
ABSTRACT: Predicting molecular properties is essential for advancing for advancing drug discovery and design. Recently, Graph Neural Networks (GNNs) have gained prominence due to their ability to ...
This repository provides the implementation of the AEGAE method for community detection in attributed graphs. AEGAE integrates Laplacian regularization and a graph autoencoder to generate robust node ...
Dr. James McCaffrey of Microsoft Research tackles the process of examining a set of source data to find data items that are different in some way from the majority of the source items. Data anomaly ...
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