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Breast cancer image classification based on H&E staining using a causal attention graph neural network model
期刊论文
Medical and Biological Engineering and Computing,2025, 卷号: 63, 期号: 7, 页码: 1965-1979
作者:
Chang, Xiaoya
;
Zhang, Zhongrong
;
Sun, Jianguo
;
Lin, Kang
;
Song, Ping' an
收藏
  |  
浏览/下载:10/0
  |  
提交时间:2025/07/22
Breast cancer
Causal intervention
Graph classification
Graph neural network
Graph-based Fingerprint Update Using Unlabelled WiFi Signals
期刊论文
Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies,2025, 卷号: 9, 期号: 1
作者:
Chiu, Ka Ho
;
Yin, Handi
;
Zhuo, Weipeng
;
Lee, Chul Ho
;
Chan, S. H.Gary
收藏
  |  
浏览/下载:55/0
  |  
提交时间:2025/05/06
Crowdsourcing
Fingerprint update
Graph Neural Network
WiFi Fingerprinting
Real-Time Coordination of Dynamic Network Reconfiguration and Volt-VAR Control in Active Distribution Network: A Graph-Aware Deep Reinforcement Learning Approach
期刊论文
IEEE Transactions on Smart Grid,2024, 卷号: 15, 期号: 3, 页码: 3288-3302
作者:
Wang, Ruoheng
;
Bi, Xiaowen
;
Bu, Siqi
收藏
  |  
浏览/下载:19/0
  |  
提交时间:2025/01/14
deep reinforcement learning (DRL)
Dynamic network reconfiguration (DNR)
graph neural network (GNN)
soft actor critic (SAC)
volt-VAR control (VVC)
TENET: Triple-enhancement based graph neural network for cell-cell interaction network reconstruction from spatial transcriptomics
期刊论文
Journal of Molecular Biology,2024, 卷号: 436, 期号: 9
作者:
Lee, Yujian
;
Xu, Yongqi
;
Gao, Peng
;
Chen, Jiaxing
收藏
  |  
浏览/下载:16/0
  |  
提交时间:2024/05/07
cell-cell interaction network reconstruction
deep learning
gene regulatory network
graph neural network
spatial transcriptomics
ToupleGDD: A Fine-Designed Solution of Influence Maximization by Deep Reinforcement Learning
期刊论文
IEEE Transactions on Computational Social Systems,2024, 卷号: 11, 期号: 2, 页码: 2210-2221
作者:
Chen, Tiantian
;
Yan, Siwen
;
Guo, Jianxiong
;
Wu, Weili
收藏
  |  
浏览/下载:25/0
  |  
提交时间:2024/05/07
Deep reinforcement learning (DRL)
generalization
graph neural networks (GNNs)
influence maximization (IM)
social network
Unraveling the role of M1 macrophage and CXCL9 in predicting immune checkpoint inhibitor efficacy through multicohort analysis and single-cell RNA sequencing
期刊论文
MedComm,2024, 卷号: 5, 期号: 3
作者:
Yu, Yunfang
;
Chen, Haizhu
;
Ouyang, Wenhao
;
Zeng, Jin
;
Huang, Hong
收藏
  |  
浏览/下载:16/0
  |  
提交时间:2024/05/07
Apolipoprotein B MRNA Editing Enzyme Catalytic Subunit 3G (APOBEC3G)
C-X-C Motif Chemokine Ligand 9 (CXCL9)
immune checkpoint inhibitors
M1 macrophage
multi-level attention graph neural network
tumor immune microenvironment
Drug-Target Interactions Prediction Based on Signed Heterogeneous Graph Neural Networks
期刊论文
Chinese Journal of Electronics,2024, 卷号: 33, 期号: 1, 页码: 231-244
作者:
Chen, Ming
;
Jiang, Yajian
;
Lei, Xiujuan
;
Pan, Yi
;
Ji, Chunyan
收藏
  |  
浏览/下载:10/0
  |  
提交时间:2024/03/12
Drug-target interactions
Graph neural networks
Link sign prediction
Signed heterogeneous network
Complexity-based graph convolutional neural network for epilepsy diagnosis in normal, acute, and chronic stages
期刊论文
Frontiers in Computational Neuroscience,2023, 卷号: 17
作者:
Zheng, Shiming
;
Zhang, Xiaopei
;
Song, Panpan
;
Hu, Yue
;
Gong, Xi
收藏
  |  
浏览/下载:7/0
  |  
提交时间:2025/04/14
chronic stage
EEG complexity measures
entropy
epilepsy diagnosis
graph convolutional neural network
Infant Cry Classification with Graph Convolutional Networks
会议论文
2021 IEEE 6th International Conference on Computer and Communication Systems, ICCCS 2021, Chengdu, 23-26 April 2021
作者:
Ji, Chunyan
;
Ming, Chen
;
Li, Bin
;
Yi, Pan
收藏
  |  
浏览/下载:8/0
  |  
提交时间:2025/07/07
graph convolutional network
graph neural network
infant cry classification
transfer learning
Attention-Based Aggregation Graph Networks for Knowledge Graph Information Transfer
会议论文
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Singapore, May 11–14, 2020
作者:
Zhao, Ming
;
Jia, Weijia
;
Huang, Yusheng
收藏
  |  
浏览/下载:16/0
  |  
提交时间:2021/07/30
Graph Attention Network
Graph Neural Network
Knowledge graph
Zero-shot learning