Results 41 to 50 of about 409,466 (266)

Dual-channel deep graph convolutional neural networks

Frontiers in Artificial Intelligence
The dual-channel graph convolutional neural networks based on hybrid features jointly model the different features of networks, so that the features can learn each other and improve the performance of various subsequent machine learning tasks.
Zhonglin Ye, Zhonglin Ye, Zhonglin Ye, Zhonglin Ye, Zhuoran Li, Zhuoran Li, Zhuoran Li, Zhuoran Li, Gege Li, Gege Li, Gege Li, Gege Li, Haixing Zhao, Haixing Zhao, Haixing Zhao, Haixing Zhao   +15 more
doaj   +1 more source

Graph Neural Network: A Comprehensive Review on Non-Euclidean Space

IEEE Access, 2021
This review provides a comprehensive overview of the state-of-the-art methods of graph-based networks from a deep learning perspective. Graph networks provide a generalized form to exploit non-euclidean space data.
Nurul A. Asif, Yeahia Sarker, Ripon K. Chakrabortty, Michael J. Ryan, Md. Hafiz Ahamed, Dip K. Saha, Faisal R. Badal, Sajal K. Das, Md. Firoz Ali, Sumaya I. Moyeen, Md. Robiul Islam, Zinat Tasneem   +11 more
doaj   +1 more source

Graph-based Neural Multi-Document Summarization

, 2017
We propose a neural multi-document summarization (MDS) system that incorporates sentence relation graphs. We employ a Graph Convolutional Network (GCN) on the relation graphs, with sentence embeddings obtained from Recurrent Neural Networks as input node
Meelu, Kshitijh, Pareek, Ayush, Radev, Dragomir, Srinivasan, Krishnan, Yasunaga, Michihiro, Zhang, Rui   +5 more
core   +1 more source

Graph neural network driven traffic prediction technology:review and challenge

物联网学报, 2021
With the rapid development of Internet of things and artificial intelligence technology, accurate analysis and prediction of traffic data have become the primary target of intelligent transportations.In recent years, the method of traffic forecasting has
Yi ZHOU, Shuting HU, Wei LI, Nan CHENG, Ning LU, Xuemin(Sherman) SHEN   +5 more
doaj   +2 more sources

MR-GNN: Multi-Resolution and Dual Graph Neural Network for Predicting Structured Entity Interactions

, 2019
Predicting interactions between structured entities lies at the core of numerous tasks such as drug regimen and new material design. In recent years, graph neural networks have become attractive.
Chen, Long, Tao, Jing, Wang, Pinghui, Xu, Nuo, Zhao, Junzhou   +4 more
core   +1 more source

Genetic attenuation of ALDH1A1 increases metastatic potential and aggressiveness in colorectal cancer

Molecular Oncology, EarlyView.
Aldehyde dehydrogenase 1A1 (ALDH1A1) is a cancer stem cell marker in several malignancies. We established a novel epithelial cell line from rectal adenocarcinoma with unique overexpression of this enzyme. Genetic attenuation of ALDH1A1 led to increased invasive capacity and metastatic potential, the inhibition of proliferation activity, and ultimately ...
Martina Poturnajova, Zuzana Kozovska, Ondrej Pos, Kristina Pavlov, Sachin Gulati, Peter Makovicky, Kristina Jakic, Monika Burikova, Eva Sedlackova, Barbora Svitkova, Silvia Tyciakova, Vojtech Bystry, Nicolas Blavet, Boris Tichy, Matej Hrnciar, Jaroslav Budis, Miroslav Tomas, Peter Dubovan, Georgina Kolnikova, Veronika Repaska, Nikoleta Mojzesova, Eva Zomborska, Daniel Pindak, Michal Mego, Tomas Szemes, Miroslava Matuskova   +25 more
wiley   +1 more source

Degree-Aware Graph Neural Network Quantization

Entropy, 2023
In this paper, we investigate the problem of graph neural network quantization. Despite the great success on convolutional neural networks, directly applying current network quantization approaches to graph neural networks faces two challenges.
Ziqin Fan, Xi Jin
doaj   +1 more source

Analyzing Learned Molecular Representations for Property Prediction

, 2019
Advancements in neural machinery have led to a wide range of algorithmic solutions for molecular property prediction. Two classes of models in particular have yielded promising results: neural networks applied to computed molecular fingerprints or expert-
Barzilay, Regina, Coley, Connor, Eiden, Philipp, Gao, Hua, Guzman-Perez, Angel, Hopper, Timothy, Jaakkola, Tommi, Jensen, Klavs, Jin, Wengong, Kelley, Brian, Mathea, Miriam, Palmer, Andrew, Settels, Volker, Swanson, Kyle, Yang, Kevin   +14 more
core   +2 more sources

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

Torsion Graph Neural Networks

IEEE Transactions on Pattern Analysis and Machine Intelligence
Geometric deep learning (GDL) models have demonstrated a great potential for the analysis of non-Euclidian data. They are developed to incorporate the geometric and topological information of non-Euclidian data into the end-to-end deep learning architectures. Motivated by the recent success of discrete Ricci curvature in graph neural network (GNNs), we
Cong Shen, Xiang Liu, Jiawei Luo, Kelin Xia   +3 more
openaire   +3 more sources