159 related articles for article (PubMed ID: 36516131)
1. Predicting adverse drug effects: A heterogeneous graph convolution network with a multi-layer perceptron approach.
Chen YH; Shih YT; Chien CS; Tsai CS
PLoS One; 2022; 17(12):e0266435. PubMed ID: 36516131
[TBL] [Abstract][Full Text] [Related]
2. MathEagle: Accurate prediction of drug-drug interaction events via multi-head attention and heterogeneous attribute graph learning.
Hou LX; Yi HC; You ZH; Chen SH; Zheng J; Kwoh CK
Comput Biol Med; 2024 Jul; 177():108642. PubMed ID: 38820777
[TBL] [Abstract][Full Text] [Related]
3. Identifying drug-target interactions via heterogeneous graph attention networks combined with cross-modal similarities.
Jiang L; Sun J; Wang Y; Ning Q; Luo N; Yin M
Brief Bioinform; 2022 Mar; 23(2):. PubMed ID: 35224614
[TBL] [Abstract][Full Text] [Related]
4. Predicting cancer drug response using parallel heterogeneous graph convolutional networks with neighborhood interactions.
Peng W; Liu H; Dai W; Yu N; Wang J
Bioinformatics; 2022 Sep; 38(19):4546-4553. PubMed ID: 35997568
[TBL] [Abstract][Full Text] [Related]
5. Graph Convolution Networks with manifold regularization for semi-supervised learning.
Kejani MT; Dornaika F; Talebi H
Neural Netw; 2020 Jul; 127():160-167. PubMed ID: 32361546
[TBL] [Abstract][Full Text] [Related]
6. Multi-type feature fusion based on graph neural network for drug-drug interaction prediction.
He C; Liu Y; Li H; Zhang H; Mao Y; Qin X; Liu L; Zhang X
BMC Bioinformatics; 2022 Jun; 23(1):224. PubMed ID: 35689200
[TBL] [Abstract][Full Text] [Related]
7. MAMF-GCN: Multi-scale adaptive multi-channel fusion deep graph convolutional network for predicting mental disorder.
Pan J; Lin H; Dong Y; Wang Y; Ji Y
Comput Biol Med; 2022 Sep; 148():105823. PubMed ID: 35872410
[TBL] [Abstract][Full Text] [Related]
8. GC-MLP: Graph Convolution MLP for Point Cloud Analysis.
Wang Y; Geng G; Zhou P; Zhang Q; Li Z; Feng R
Sensors (Basel); 2022 Dec; 22(23):. PubMed ID: 36502189
[TBL] [Abstract][Full Text] [Related]
9. Multi-label zero-shot learning with graph convolutional networks.
Ou G; Yu G; Domeniconi C; Lu X; Zhang X
Neural Netw; 2020 Dec; 132():333-341. PubMed ID: 32977278
[TBL] [Abstract][Full Text] [Related]
10. MVS-GCN: A prior brain structure learning-guided multi-view graph convolution network for autism spectrum disorder diagnosis.
Wen G; Cao P; Bao H; Yang W; Zheng T; Zaiane O
Comput Biol Med; 2022 Mar; 142():105239. PubMed ID: 35066446
[TBL] [Abstract][Full Text] [Related]
11. An Explainable Framework for Predicting Drug-Side Effect Associations via Meta-Path-Based Feature Learning in Heterogeneous Information Network.
Zhao W; Yao W; Jiang X; He T; Shi C; Hu X
IEEE/ACM Trans Comput Biol Bioinform; 2023; 20(6):3635-3647. PubMed ID: 37616131
[TBL] [Abstract][Full Text] [Related]
12. GCHN-DTI: Predicting drug-target interactions by graph convolution on heterogeneous networks.
Wang W; Liang S; Yu M; Liu D; Zhang H; Wang X; Zhou Y
Methods; 2022 Oct; 206():101-107. PubMed ID: 36058415
[TBL] [Abstract][Full Text] [Related]
13. Optimizing neural networks for medical data sets: A case study on neonatal apnea prediction.
Shirwaikar RD; Acharya U D; Makkithaya K; M S; Srivastava S; Lewis U LES
Artif Intell Med; 2019 Jul; 98():59-76. PubMed ID: 31521253
[TBL] [Abstract][Full Text] [Related]
14. Integrating specific and common topologies of heterogeneous graphs and pairwise attributes for drug-related side effect prediction.
Xuan P; Wang M; Liu Y; Wang D; Zhang T; Nakaguchi T
Brief Bioinform; 2022 May; 23(3):. PubMed ID: 35470853
[TBL] [Abstract][Full Text] [Related]
15. Hi-GCN: A hierarchical graph convolution network for graph embedding learning of brain network and brain disorders prediction.
Jiang H; Cao P; Xu M; Yang J; Zaiane O
Comput Biol Med; 2020 Dec; 127():104096. PubMed ID: 33166800
[TBL] [Abstract][Full Text] [Related]
16. MGDDI: A multi-scale graph neural networks for drug-drug interaction prediction.
Geng G; Wang L; Xu Y; Wang T; Ma W; Duan H; Zhang J; Mao A
Methods; 2024 Aug; 228():22-29. PubMed ID: 38754712
[TBL] [Abstract][Full Text] [Related]
17. DTI-HETA: prediction of drug-target interactions based on GCN and GAT on heterogeneous graph.
Shao K; Zhang Y; Wen Y; Zhang Z; He S; Bo X
Brief Bioinform; 2022 May; 23(3):. PubMed ID: 35380622
[TBL] [Abstract][Full Text] [Related]
18. A novel machine learning model based on sparse structure learning with adaptive graph regularization for predicting drug side effects.
Liang X; Li J; Fu Y; Qu L; Tan Y; Zhang P
J Biomed Inform; 2022 Aug; 132():104131. PubMed ID: 35840061
[TBL] [Abstract][Full Text] [Related]
19. IHG-MA: Inductive heterogeneous graph multi-agent reinforcement learning for multi-intersection traffic signal control.
Yang S; Yang B; Kang Z; Deng L
Neural Netw; 2021 Jul; 139():265-277. PubMed ID: 33838602
[TBL] [Abstract][Full Text] [Related]
20. A Convolutional Neural Network and Graph Convolutional Network Based Framework for Classification of Breast Histopathological Images.
Gao Z; Lu Z; Wang J; Ying S; Shi J
IEEE J Biomed Health Inform; 2022 Jul; 26(7):3163-3173. PubMed ID: 35196251
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
