Results 91 to 100 of about 4,082,283 (306)

Arginine methylation as a regulatory ratchet in cancer: From substrate selection to malignant‐state stabilization

Molecular Oncology, EarlyView.
Arginine methylation can be viewed as a persistence‐prone post‐translational modification regulated by a network of PRMTs. Competitive and compensatory interactions among PRMTs can redistribute methylation across substrate pools shaped by sequence, structural, spatial, and environmental layers, reinforcing RNA‐processing, chromatin, and signaling ...
So Hyun Kwon, Ji Min Lee
wiley   +1 more source

Hierarchical Graph Neural Networks

CoRR, 2021
Over the recent years, Graph Neural Networks have become increasingly popular in network analytic and beyond. With that, their architecture noticeable diverges from the classical multi-layered hierarchical organization of the traditional neural networks.
openaire   +3 more sources

Graph neural network structural limitation for thermal simulation and architecture optimization through rating system

Advanced Modeling and Simulation in Engineering Sciences
Graph neural networks are well suited for physics based simulation. Among other features, graphs can accurately represent thermal effects, with energy conservation operating on the nodes (vertices) and heat flow coursing through edges.
Pierre Hembert, Chady Ghnatios, Julien Cotton, Francisco Chinesta   +3 more
doaj   +1 more source

Graph stochastic neural networks for semi-supervised learning

, 2020
Graph Neural Networks (GNNs) have achieved remarkable performance in the task of the semi-supervised node classification. However, most existing models learn a deterministic classification function, which lack sufficient flexibility to explore better ...
Wang, H, Zhou, C, Wang, J, Wu, J, Chen, X, Pan, S   +5 more
core  

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, Marco Corazzari, Iman Osman, Jane Armstrong, Noel Carter   +4 more
wiley   +1 more source

Deep learning-based spatial-temporal graph neural networks for price movement classification in crude oil and precious metal markets

Machine Learning with Applications
In this study, we adapt three spatial-temporal graph neural network models to the unique characteristics of crude oil, gold, and silver markets for forecasting purposes.
Parisa Foroutan, Salim Lahmiri
doaj   +1 more source

Quantum walk neural networks with feature dependent coins

Applied Network Science, 2019
Recent neural networks designed to operate on graph-structured data have proven effective in many domains. These graph neural networks often diffuse information using the spatial structure of the graph.
Stefan Dernbach, Arman Mohseni-Kabir, Siddharth Pal, Miles Gepner, Don Towsley   +4 more
doaj   +1 more source

A Comparison between Recursive Neural Networks and Graph Neural Networks

, 2006
Recursive neural networks (RNNs) and graph neural networks (GNNs) are two connectionist models that can directly process graphs. RNNs and GNNs exploit a similar processing framework, but they can be applied to different input domains.
V. Di Massa, Lorenzo Sarti, L. Sarti, Marco Maggini, G. Monfardini, Vincenzo Di Massa, Scarselli, F., Di Massa, V., Marco Gori, F. Scarselli, Sarti, L., Monfardini, G., Maggini, M., M. Maggini, Gabriele Monfardini, Franco Scarselli, M. Gori, Gori, M.   +17 more
core   +1 more source

From energy provision to protein synthesis: Tunnelling nanotubes as mediators of intercellular metabolic cooperation in cancer

FEBS Open Bio, EarlyView.
The cytoskeleton‐mediated transport of mitochondria via tunnelling nanotubes restores respiration, increases ATP production, rescues cells from apoptosis, activates the AKT/mTOR signalling pathway, promotes cell migration and invasiveness, contributes to cancer progression and treatment resistance.
Stanislava Martínková, Jan Trnka
wiley   +1 more source

AAGCN: a graph convolutional neural network with adaptive feature and topology learning

Scientific Reports
In recent years, there has been a growing prevalence of deep learning in various domains, owing to advancements in information technology and computing power.
Bin Wang, Bodong Cai, Jinfang Sheng, Wenzhe Jiao   +3 more
doaj   +1 more source