Results 51 to 60 of about 409,466 (266)
Graphs, Convolutions, and Neural Networks: From Graph Filters to Graph Neural Networks
IEEE Signal Processing Magazine, 2020 Signal Processing ...
Gama, F. (author) +3 more
openaire +6 more sources
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 +36 more
wiley +1 more source
Situation Recognition with Graph Neural Networks
, 2017 We address the problem of recognizing situations in images. Given an image, the task is to predict the most salient verb (action), and fill its semantic roles such as who is performing the action, what is the source and target of the action, etc ...
Fidler, Sanja +5 more
core +1 more source
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 +5 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) +1 more
openaire +4 more sources
Approach to the fake news detection using the graph neural networks
Journal of Edge Computing, 2023 The experience of Russia’s war against Ukraine demonstrates the relevance and necessity of understanding the problems of constant disinformation, the spread of propaganda, and the implementation of destructive negative psychological influence. The issue
Ihor A. Pilkevych +3 more
doaj +1 more source
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 +14 more
wiley +1 more source
IEEE Transactions on Neural Networks and Learning SystemsThe 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 +6 more
openaire +4 more sources
Topological Properties of Neuromorphic Nanowire Networks
Frontiers in Neuroscience, 2020 Graph theory has been extensively applied to the topological mapping of complex networks, ranging from social networks to biological systems. Graph theory has increasingly been applied to neuroscience as a method to explore the fundamental structural and
Alon Loeffler +8 more
doaj +1 more source
Kernel Graph Convolutional Neural Networks
, 2018 Graph kernels have been successfully applied to many graph classification problems. Typically, a kernel is first designed, and then an SVM classifier is trained based on the features defined implicitly by this kernel.
Meladianos, Polykarpos +4 more
core +2 more sources