Results 61 to 70 of about 409,466 (266)
NKCC1: A key regulator of glioblastoma progression
Molecular Oncology, EarlyView.Glioblastoma (GBM) progression is driven by disrupted chloride cotransporter homeostasis. NKCC1 is highly expressed in stem‐like, astrocytic, and progenitor cells, correlating with earlier recurrence, while overall survival remains unaffected. NKCC1 serves as a prognostic marker and potential therapeutic target, linking chloride transporter imbalance ...
Anja Thomsen +5 more
wiley +1 more source
Stochastic graph recurrent neural network
Neurocomputing, 2022 Representation learning over graph structure data has been widely studied due to its wide application prospects. However, previous methods mainly focus on static graphs while many real-world graphs evolve over time. Modeling such evolution is important for predicting properties of unseen networks.
Yan, Tijin +3 more
openaire +2 more sources
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
Graph Convolutional Networks for Text Classification
, 2018 Text classification is an important and classical problem in natural language processing. There have been a number of studies that applied convolutional neural networks (convolution on regular grid, e.g., sequence) to classification.
Luo, Yuan, Mao, Chengsheng, Yao, Liang
core +1 more source
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
MGATs: Motif-Based Graph Attention Networks
MathematicsIn 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 +3 more
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 +4 more
doaj +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
FEBS Open Bio, EarlyView.A tri‐culture of iPSC‐derived neurons, astrocytes, and microglia treated with ferroptosis inducers as an Induced ferroptosis model was characterized by scRNA‐seq, cell survival, and cytokine release assays. This analysis revealed diverse microglial transcriptomic changes, indicating that the system captures key aspects of the complex cellular ...
Hongmei Lisa Li +6 more
wiley +1 more source
Advanced Modeling and Simulation in Engineering SciencesGraph 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 +3 more
doaj +1 more source