These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.
150 related articles for article (PubMed ID: 34699377)
1. NPI-RGCNAE: Fast Predicting ncRNA-Protein Interactions Using the Relational Graph Convolutional Network Auto-Encoder. Yu H; Shen ZA; Du PF IEEE J Biomed Health Inform; 2022 Apr; 26(4):1861-1871. PubMed ID: 34699377 [TBL] [Abstract][Full Text] [Related]
2. NPI-GNN: Predicting ncRNA-protein interactions with deep graph neural networks. Shen ZA; Luo T; Zhou YK; Yu H; Du PF Brief Bioinform; 2021 Sep; 22(5):. PubMed ID: 33822882 [TBL] [Abstract][Full Text] [Related]
3. NPI-DCGNN: An Accurate Tool for Identifying ncRNA-Protein Interactions Using a Dual-Channel Graph Neural Network. Zhang X; Zhao L; Chai Z; Wu H; Yang W; Li C; Jiang Y; Liu Q J Comput Biol; 2024 Aug; 31(8):742-756. PubMed ID: 38923911 [TBL] [Abstract][Full Text] [Related]
4. Predicting ncRNA-protein interactions based on dual graph convolutional network and pairwise learning. Zhuo L; Song B; Liu Y; Li Z; Fu X Brief Bioinform; 2022 Sep; 23(5):. PubMed ID: 36063562 [TBL] [Abstract][Full Text] [Related]
5. Exploring noncoding RNAs in thyroid cancer using a graph convolutional network approach. Xu H; Hu X; Yan X; Zhong W; Yin D; Gai Y Comput Biol Med; 2022 Jun; 145():105447. PubMed ID: 35430557 [TBL] [Abstract][Full Text] [Related]
6. EDLMFC: an ensemble deep learning framework with multi-scale features combination for ncRNA-protein interaction prediction. Wang J; Zhao Y; Gong W; Liu Y; Wang M; Huang X; Tan J BMC Bioinformatics; 2021 Mar; 22(1):133. PubMed ID: 33740884 [TBL] [Abstract][Full Text] [Related]
7. Exploring ncRNA-Drug Sensitivity Associations via Graph Contrastive Learning. Hu X; Jiang Y; Deng L IEEE/ACM Trans Comput Biol Bioinform; 2024; 21(5):1380-1389. PubMed ID: 38578855 [TBL] [Abstract][Full Text] [Related]
8. BGFE: A Deep Learning Model for ncRNA-Protein Interaction Predictions Based on Improved Sequence Information. Zhan ZH; Jia LN; Zhou Y; Li LP; Yi HC Int J Mol Sci; 2019 Feb; 20(4):. PubMed ID: 30813451 [TBL] [Abstract][Full Text] [Related]
9. A model for predicting ncRNA-protein interactions based on graph neural networks and community detection. Zhuo L; Chen Y; Song B; Liu Y; Su Y Methods; 2022 Nov; 207():74-80. PubMed ID: 36108992 [TBL] [Abstract][Full Text] [Related]
10. ncRNA Coding Potential Prediction Using BiLSTM and Transformer Encoder-Based Model. Zhang J; Lu H; Jiang Y; Ma Y; Deng L J Chem Inf Model; 2024 Aug; 64(16):6712-6722. PubMed ID: 39120528 [TBL] [Abstract][Full Text] [Related]
11. IPMiner: hidden ncRNA-protein interaction sequential pattern mining with stacked autoencoder for accurate computational prediction. Pan X; Fan YX; Yan J; Shen HB BMC Genomics; 2016 Aug; 17():582. PubMed ID: 27506469 [TBL] [Abstract][Full Text] [Related]
12. Predicting potential interactions between lncRNAs and proteins via combined graph auto-encoder methods. Zhao J; Sun J; Shuai SC; Zhao Q; Shuai J Brief Bioinform; 2023 Jan; 24(1):. PubMed ID: 36515153 [TBL] [Abstract][Full Text] [Related]
13. GCMCDTI: Graph convolutional autoencoder framework for predicting drug-target interactions based on matrix completion. Li J; Zhang C; Li Z; Nie R; Han P; Yang W; Liao H J Bioinform Comput Biol; 2022 Oct; 20(5):2250023. PubMed ID: 36350601 [TBL] [Abstract][Full Text] [Related]
14. HeadTailTransfer: An efficient sampling method to improve the performance of graph neural network method in predicting sparse ncRNA-protein interactions. Wei J; Zhuo L; Pan S; Lian X; Yao X; Fu X Comput Biol Med; 2023 May; 157():106783. PubMed ID: 36958237 [TBL] [Abstract][Full Text] [Related]
15. LRGCPND: Predicting Associations between ncRNA and Drug Resistance via Linear Residual Graph Convolution. Li Y; Wang R; Zhang S; Xu H; Deng L Int J Mol Sci; 2021 Sep; 22(19):. PubMed ID: 34638849 [TBL] [Abstract][Full Text] [Related]
16. GATLGEMF: A graph attention model with line graph embedding multi-complex features for ncRNA-protein interactions prediction. Yan J; Qu W; Li X; Wang R; Tan J Comput Biol Chem; 2024 Feb; 108():108000. PubMed ID: 38070456 [TBL] [Abstract][Full Text] [Related]
17. A Deep Learning Framework for Robust and Accurate Prediction of ncRNA-Protein Interactions Using Evolutionary Information. Yi HC; You ZH; Huang DS; Li X; Jiang TH; Li LP Mol Ther Nucleic Acids; 2018 Jun; 11():337-344. PubMed ID: 29858068 [TBL] [Abstract][Full Text] [Related]
18. Computational Methods for Predicting ncRNA-protein Interactions. Zhang SW; Fan XN Med Chem; 2017; 13(6):515-525. PubMed ID: 28494725 [TBL] [Abstract][Full Text] [Related]
19. Graph Neural Network with Self-Supervised Learning for Noncoding RNA-Drug Resistance Association Prediction. Zheng J; Qian Y; He J; Kang Z; Deng L J Chem Inf Model; 2022 Aug; 62(15):3676-3684. PubMed ID: 35838124 [TBL] [Abstract][Full Text] [Related]
20. DRGCNCDA: Predicting circRNA-disease interactions based on knowledge graph and disentangled relational graph convolutional network. Lan W; Zhang H; Dong Y; Chen Q; Cao J; Peng W; Liu J; Li M Methods; 2022 Dec; 208():35-41. PubMed ID: 36280134 [TBL] [Abstract][Full Text] [Related] [Next] [New Search]