Results 81 to 90 of about 110,849 (310)

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

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

MGATs: Motif-Based Graph Attention Networks

Mathematics
In recent years, graph convolutional neural networks (GCNs) have become a popular research topic due to their outstanding performance in various complex network data mining tasks.
Jinfang Sheng, Yufeng Zhang, Bin Wang, Yaoxing Chang   +3 more
doaj   +1 more source

Scalable algorithms for physics-informed neural and graph networks

Data-Centric Engineering, 2022
Physics-informed machine learning (PIML) has emerged as a promising new approach for simulating complex physical and biological systems that are governed by complex multiscale processes for which some data are also available.
Khemraj Shukla, Mengjia Xu, Nathaniel Trask, George E. Karniadakis   +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  

Understanding Pooling in Graph Neural Networks

, 2021
Inspired by the conventional pooling layers in convolutional neural networks, many recent works in the field of graph machine learning have introduced pooling operators to reduce the size of graphs. The great variety in the literature stems from the many
Grattarola, Daniele, Zambon, Daniele, Daniele Zambon, Bianchi, Filippo Maria, Filippo Maria Bianchi, Alippi, Cesare, Cesare Alippi, Daniele Grattarola   +7 more
core   +1 more source

Diffusion Spectrum Imaging Maps Early Axonal Loss and a Unique Progressive Signal in Neuronal Intranuclear Inclusion Disease

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective To delineate specific in vivo white matter pathology in neuronal intranuclear inclusion disease (NIID) using diffusion spectrum imaging (DSI) and define its clinical relevance. Methods DSI was performed on 42 NIID patients and 38 matched controls.
Kaiyan Jiang, Yixiu Pei, Xiaobao Hu, Ying Xiong, Fang Wang, Yanyan Yu, Min Zhu, Dandan Tan, Meihong Zhou, Daojun Hong, Fuqing Zhou   +10 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

Development of a graph convolutional neural network model for efficient prediction of protein-ligand binding affinities.

PLoS ONE, 2021
Prediction of protein-ligand interactions is a critical step during the initial phase of drug discovery. We propose a novel deep-learning-based prediction model based on a graph convolutional neural network, named GraphBAR, for protein-ligand binding ...
Jeongtae Son, Dongsup Kim
doaj   +1 more source