Results 71 to 80 of about 110,849 (310)
Approach to the fake news detection using the graph neural networks
Journal of Edge Computing, 2023 The experience of Russia’s war against Ukraine demonstrates the relevance and necessity of understanding the problems of constant disinformation, the spread of propaganda, and the implementation of destructive negative psychological influence. The issue
Ihor A. Pilkevych +3 more
doaj +1 more source
Deep recurrent graph neural networks [PDF]
, 2020 Graph Neural Networks (GNN) show good results in classification and regression on graphs, notwithstanding most GNN models use a limited depth. In fact, they are composed of only a few stacked graph convolutional layers.
Pasa L., Sperduti A., Navarin N.
core
Molecular Oncology, EarlyView.Dormant cancer cells can hide in distant organs for years, evading treatment and the immune system. This review highlights how signals from the surrounding tissue and immune environment keep these cells inactive or trigger their reawakening. Understanding these mechanisms may help develop therapies to eliminate or control dormant cells and prevent ...
Kanishka Tiwary +1 more
wiley +1 more source
Topological Properties of Neuromorphic Nanowire Networks
Frontiers in Neuroscience, 2020 Graph theory has been extensively applied to the topological mapping of complex networks, ranging from social networks to biological systems. Graph theory has increasingly been applied to neuroscience as a method to explore the fundamental structural and
Alon Loeffler +8 more
doaj +1 more source
Graph neural networks for materials science and chemistry
Communications Materials, 2022 Graph neural networks are machine learning models that directly access the structural representation of molecules and materials. This Review discusses state-of-the-art architectures and applications of graph neural networks in materials science and ...
Patrick Reiser +10 more
doaj +1 more source
A Tropical View of Graph Neural Networks
, 2023 Learning dynamic programming algorithms with Graph Neural Networks (GNNs) is a research direction which is increasingly gaining popularity. Prior work has demonstrated that in order to learn such algorithms, it is necessary to have an ``alignment ...
Bacciu, Davide +2 more
core +1 more source
Molecular Oncology, EarlyView.Single‐cell multi‐omics reveals epigenetic heterogeneity across therapy‐adaptive tumor states, including quiescent/dormant, drug‐tolerant persister, and EMT‐like phenotypes. By linking regulatory features with state‐associated biomarkers, these approaches inform biomarker‐guided therapeutic strategies for evolving tumors.
Hee Jung Kim +3 more
wiley +1 more source
Graph Convolutional Network for 3D Object Pose Estimation in a Point Cloud
Sensors, 2022 Graph Neural Networks (GNNs) are neural networks that learn the representation of nodes and associated edges that connect it to every other node while maintaining graph representation.
Tae-Won Jung +5 more
doaj +1 more source
Loss of AMBRA1 activates MAPK and angiogenesis signaling pathways in melanoma cells
FEBS Open Bio, EarlyView.Loss of AMBRA1 in melanoma cells activates multiple oncogenic pathways associated with tumor progression. Transcriptomic and protein network analyses revealed that AMBRA1 depletion enhances MAPK/ERK signaling, angiogenesis, TGF‐β/EMT signaling, and Wnt/axon guidance pathways.
Milad Ibrahim +4 more
wiley +1 more source
Entropy, 2023 Identifying influential nodes is a key research topic in complex networks, and there have been many studies based on complex networks to explore the influence of nodes.
Ying Xi, Xiaohui Cui
doaj +1 more source