Results 51 to 60 of about 437,099 (249)

RIPK4 function interferes with melanoma cell adhesion and metastasis

Molecular Oncology, EarlyView.
RIPK4 promotes melanoma growth and spread. RIPK4 levels increase as skin lesions progress to melanoma. CRISPR/Cas9‐mediated deletion of RIPK4 causes melanoma cells to form less compact spheroids, reduces their migratory and invasive abilities and limits tumour growth and dissemination in mouse models.
Norbert Wronski, Sławomir Lasota, Ewelina Madej, Anna A. Brożyna, Małgorzata Szczygieł, Agnieszka Harazin‐Lechowska, Jan Czerbniak, Janusz Rys, Jaroslaw Czyz, Agnieszka Wolnicka‐Glubisz   +9 more
wiley   +1 more source

Graph-Time Convolutional Neural Networks

2021 IEEE Data Science and Learning Workshop (DSLW), 2021
Spatiotemporal data can be represented as a process over a graph, which captures their spatial relationships either explicitly or implicitly. How to leverage such a structure for learning representations is one of the key challenges when working with graphs. In this paper, we represent the spatiotemporal relationships through product graphs and develop
Isufi, E. (author), Mazzola, Gabriele (author)   +1 more
openaire   +4 more sources

Somatic mutational landscape in von Hippel–Lindau familial hemangioblastoma

Molecular Oncology, EarlyView.
The causes of central nervous system (CNS) hemangioblastoma in Von Hippel–Lindau (vHL) disease are unclear. We used Whole Exome Sequencing (WES) on familial hemangioblastoma to investigate events that underlie tumor development. Our findings suggest that VHL loss creates a permissive environment for tumor formation, while additional alterations ...
Maja Dembic, Anne Nørremølle, Lilian Bomme Ousager, Lars van Brakel Andersen, Marie Louise Mølgaard Binderup, Mads Thomassen   +5 more
wiley   +1 more source

Deep neural networks on graph signals for brain imaging analysis

, 2017
Brain imaging data such as EEG or MEG are high-dimensional spatiotemporal data often degraded by complex, non-Gaussian noise. For reliable analysis of brain imaging data, it is important to extract discriminative, low-dimensional intrinsic representation
Cheung, Ngai-Man, Guo, Yiluan, Nejati, Hossein   +2 more
core   +1 more source

Graph Kernel Neural Networks

IEEE Transactions on Neural Networks and Learning Systems
The convolution operator at the core of many modern neural architectures can effectively be seen as performing a dot product between an input matrix and a filter. While this is readily applicable to data such as images, which can be represented as regular grids in the Euclidean space, extending the convolution operator to work on graphs proves more ...
Cosmo, Luca, Minello, Giorgia, Bicciato, Alessandro, Bronstein, Michael M., Rodolà, Emanuele, Rossi, Luca, Torsello, Andrea   +6 more
openaire   +4 more sources

Deciphering transcriptional plasticity in pancreatic ductal adenocarcinoma reveals alterations in sensory neuron innervation

Molecular Oncology, EarlyView.
Pancreatic sensory neurons innervating healthy and PDAC tissue were retrogradely labeled and profiled by single‐cell RNA sequencing. Tumor‐associated innervation showed a dominant neurofilament‐positive subtype, altered mitochondrial gene signatures, and reduced non‐peptidergic neurons.
Elena Genova, Michele Montrone, Uday Rangaswamy, Francesco Diversi, Irene Schiavo, Denise Ferrarini, Roberta Di Florio, Irene Longo, Michele Coscia, Nicola Zamboni, Giorgia Demontis, Lisa Veghini, Vincenzo Corbo, Remo Sanges, Paul Heppenstall   +14 more
wiley   +1 more source

A Graph Neural Network Assisted Monte Carlo Tree Search Approach to Traveling Salesman Problem

IEEE Access, 2020
We tackle the classical traveling salesman problem (TSP) by combining a graph neural network and Monte Carlo Tree Search. We adopt a greedy algorithm framework to derive a promising tour by adding the vertices successively.
Zhihao Xing, Shikui Tu
doaj   +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, Zhang, Hongwei, Li, Zirui, Xia, Yuanqing   +3 more
openaire   +2 more sources

Metformin promotes mitochondrial integrity through AMPK‐signaling in Leber's hereditary optic neuropathy

FEBS Open Bio, EarlyView.
Metformin mediates mitochondrial quality control in Leber's hereditary optic neuropathy (LHON) fibroblasts carrying mtDNA mutations. At therapeutic levels, metformin activates AMPK signaling to restore mitochondrial dynamics by promoting fusion and restraining fission, while preserving mitochondrial mass, enhancing autophagy/mitophagy and biogenesis ...
Chatnapa Panusatid, Rapasviranda Soiyangsuk, Maneeluck Tanadjindarat, Chayanon Peerapittayamongkol   +3 more
wiley   +1 more source

Improving data security with the utilization of matrix columnar transposition techniques [PDF]

E3S Web of Conferences
The Graph Neural Network (GNN) is an advanced use of graph theory that is used to address complex network problems. The application of Graph Neural Networks allows the development of a network by the modification of weights associated with the vertices ...
Tulus, Sy Syafrizal, Sugeng Kiki A., Simanjuntak Rinovia, Marpaung J.L.   +4 more
doaj   +1 more source