Results 91 to 100 of about 184,942 (303)

Enabling prediction via multi-layer graph inference and sampling

, 2019
In this work we propose a novel method to efficiently predict dynamic signals over both space and time, exploiting the theory of sampling and recovery of band-limited graph signals.
Di Lorenzo P., Barbarossa S., Sardellitti S.   +2 more
core   +1 more source

Reeb Graph of Sample Thickenings

CoRR
18 pages, 1 ...
Håvard Bakke Bjerkevik, Nello Blaser, Lars M. Salbu   +2 more
openaire   +2 more sources

Network divergence analysis identifies adaptive gene modules and two orthogonal vulnerability axes in pancreatic cancer

Molecular Oncology, EarlyView.
Tumors contain diverse cellular states whose behavior is shaped by context‐dependent gene coordination. By comparing gene–gene relationships across biological contexts, we identify adaptive transcriptional modules that reorganize into distinct vulnerability axes.
Brian Nelson, Lyanne Delgado‐Coka, Natalia Marchenko, Luisa F. Escobar‐Hoyos, Kenneth R. Shroyer, Alisa Yurovsky, Trey Ideker, Gábor Balázsi, Thomas MacCarthy, Scott Powers   +9 more
wiley   +1 more source

Collaborative Filtering Recommendation-Based Random Negative Sampling and Graph Attention

IEEE Access
In this era of information overload, recommendation systems significantly enhance the efficiency of information delivery, better meeting the needs of users. Currently, GCN-based recommendation systems typically use degree normalization or mean pooling to
Weiqiang Li, Xianghui Li, Xiaowen Liu, Xinhuan Chen, Ming Ma   +4 more
doaj   +1 more source

COMP–PMEPA1 axis promotes epithelial‐to‐mesenchymal transition in breast cancer cells

Molecular Oncology, EarlyView.
This study reveals that cartilage oligomeric matrix protein (COMP) promotes epithelial‐to‐mesenchymal transition (EMT) in breast cancer. We identify PMEPA1 (protein TMEPAI) as a novel COMP‐binding partner that mediates EMT via binding to the TSP domains of COMP, establishing the COMP–PMEPA1 axis as a key EMT driver in breast cancer.
Konstantinos S. Papadakos, Gilar Gorji‐bahri, Lejla Gradjan, Julia Zajac, Kacper Gil, Yvonne Thokozile Nduku, Anna M. Blom   +6 more
wiley   +1 more source

Hierarchical Graph Transformer with Adaptive Node Sampling

, 2022
The Transformer architecture has achieved remarkable success in a number of domains including natural language processing and computer vision. However, when it comes to graph-structured data, transformers have not achieved competitive performance ...
Hu, Qingyong, Zhang, Zaixi, Lee, Chee Kong, Liu, Qi   +3 more
core   +1 more source

WalkGCN: a biased sampling strategy for GNNs on non-attributed graphs

Journal of Big Data
Graph Neural Networks (GNNs) typically assume the presence of node attributes to capture interactions in a graph structure. However, real-world graph data often has incomplete or completely-missing attribute information.
Mincheol Shin, Taeyoung Choe, Yejong Ryu, Yanggon Kim, Mucheol Kim   +4 more
doaj   +1 more source

EDNRB‐dependent endothelin signaling reduces proliferation and promotes proneural‐to‐mesenchymal transition in gliomas

Molecular Oncology, EarlyView.
Glioma cells mainly express the endothelin receptor EDNRB, while EDNRA is restricted to a perivascular tumor subpopulation. Endothelin signaling reduces glioma cell proliferation while promoting migration and a proneural‐to‐mesenchymal transition associated with poor prognosis. This pathway activates Ca2+, K+, ERK, and STAT3 signalings and is regulated
Donovan Pineau, Leonor Garcia, Hugo Arnold, Antonija Hanžek, Maialen Arrieta, Laurent R Gauthier, Christine Granotier‐Beckers, François D Boussin, Amaury Herbet, Marie Hautière, Valentin Asei‐Ceschino, Clémentin Jacques, Laura Brard, Thomas Harnois, Valérie Coronas, Bruno Constantin, Aurélien Chatelier, Jean Chemin, Serge Urbach, Martial Seveno, Szimonetta Hideg, Chantal Ripoll, Kasandra Aguilar‐Cázarez, Min Zheng, Guo‐Hao Huang, Sheng‐Qing Lv, Lei Zhang, Philippe Rondard, Laurent Prezeau, Jean‐Philippe Pin, Hugues Duffau, Luc Bauchet, Valérie Rigau, Franck Denat, Charles Truillet, Didier Boquet, Jean‐Philippe Hugnot   +36 more
wiley   +1 more source

Sampling Method for Generalized Graph Signals With Pre-Selected Vertices via DC Optimization

IEEE Open Journal of Signal Processing
This paper proposes a method for vertex-wise aggregation sampling of a broad class of graph signals, designed to attain the best possible recovery based on the generalized sampling theory.
Keitaro Yamashita, Kazuki Naganuma, Shunsuke Ono   +2 more
doaj   +1 more source

Optimal Sampling for Dynamic Complex Networks With Graph-Bandlimited Initialization

IEEE Access, 2019
Many engineering, social, and biological complex systems consist of dynamical elements connected via a large-scale network. Monitoring the network’s dynamics is essential for a variety of maintenance and scientific purposes.
Zhuangkun Wei, Bin Li, Weisi Guo
doaj   +1 more source