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.
472 related articles for article (PubMed ID: 34891172)
1. HINGRL: predicting drug-disease associations with graph representation learning on heterogeneous information networks. Zhao BW; Hu L; You ZH; Wang L; Su XR Brief Bioinform; 2022 Jan; 23(1):. PubMed ID: 34891172 [TBL] [Abstract][Full Text] [Related]
2. Drug-disease association prediction using semantic graph and function similarity representation learning over heterogeneous information networks. Zhao BW; Su XR; Yang Y; Li DX; Li GD; Hu PW; Zhao YG; Hu L Methods; 2023 Dec; 220():106-114. PubMed ID: 37972913 [TBL] [Abstract][Full Text] [Related]
3. Hierarchical Negative Sampling Based Graph Contrastive Learning Approach for Drug-Disease Association Prediction. Wang Y; Song J; Dai Q; Duan X IEEE J Biomed Health Inform; 2024 May; 28(5):3146-3157. PubMed ID: 38294927 [TBL] [Abstract][Full Text] [Related]
4. RLFDDA: a meta-path based graph representation learning model for drug-disease association prediction. Zhang ML; Zhao BW; Su XR; He YZ; Yang Y; Hu L BMC Bioinformatics; 2022 Dec; 23(1):516. PubMed ID: 36456957 [TBL] [Abstract][Full Text] [Related]
5. A geometric deep learning framework for drug repositioning over heterogeneous information networks. Zhao BW; Su XR; Hu PW; Ma YP; Zhou X; Hu L Brief Bioinform; 2022 Nov; 23(6):. PubMed ID: 36125202 [TBL] [Abstract][Full Text] [Related]
6. SNF-NN: computational method to predict drug-disease interactions using similarity network fusion and neural networks. Jarada TN; Rokne JG; Alhajj R BMC Bioinformatics; 2021 Jan; 22(1):28. PubMed ID: 33482713 [TBL] [Abstract][Full Text] [Related]
7. Graph-DTI: A New Model for Drug-target Interaction Prediction Based on Heterogenous Network Graph Embedding. Qu X; Du G; Hu J; Cai Y Curr Comput Aided Drug Des; 2024; 20(6):1013-1024. PubMed ID: 37448360 [TBL] [Abstract][Full Text] [Related]
8. NEDD: a network embedding based method for predicting drug-disease associations. Zhou R; Lu Z; Luo H; Xiang J; Zeng M; Li M BMC Bioinformatics; 2020 Sep; 21(Suppl 13):387. PubMed ID: 32938396 [TBL] [Abstract][Full Text] [Related]
9. Link Prediction Only With Interaction Data and its Application on Drug Repositioning. Liu J; Zuo Z; Wu G IEEE Trans Nanobioscience; 2020 Jul; 19(3):547-555. PubMed ID: 32340956 [TBL] [Abstract][Full Text] [Related]
10. A network integration approach for drug-target interaction prediction and computational drug repositioning from heterogeneous information. Luo Y; Zhao X; Zhou J; Yang J; Zhang Y; Kuang W; Peng J; Chen L; Zeng J Nat Commun; 2017 Sep; 8(1):573. PubMed ID: 28924171 [TBL] [Abstract][Full Text] [Related]
11. Exploring potential circRNA biomarkers for cancers based on double-line heterogeneous graph representation learning. Zhang Y; Wang Z; Wei H; Chen M BMC Med Inform Decis Mak; 2024 Jun; 24(1):159. PubMed ID: 38844961 [TBL] [Abstract][Full Text] [Related]
12. DTiGNN: Learning drug-target embedding from a heterogeneous biological network based on a two-level attention-based graph neural network. Muniyappan S; Rayan AXA; Varrieth GT Math Biosci Eng; 2023 Mar; 20(5):9530-9571. PubMed ID: 37161255 [TBL] [Abstract][Full Text] [Related]
13. Drug-Disease Association Prediction Using Heterogeneous Networks for Computational Drug Repositioning. Kim Y; Jung YS; Park JH; Kim SJ; Cho YR Biomolecules; 2022 Oct; 12(10):. PubMed ID: 36291706 [TBL] [Abstract][Full Text] [Related]
14. Prediction of drug-disease associations by integrating common topologies of heterogeneous networks and specific topologies of subnets. Gao L; Cui H; Zhang T; Sheng N; Xuan P Brief Bioinform; 2022 Jan; 23(1):. PubMed ID: 34850815 [TBL] [Abstract][Full Text] [Related]
15. Partner-Specific Drug Repositioning Approach Based on Graph Convolutional Network. Sun X; Wang B; Zhang J; Li M IEEE J Biomed Health Inform; 2022 Nov; 26(11):5757-5765. PubMed ID: 35921345 [TBL] [Abstract][Full Text] [Related]
16. Computational drug repositioning using meta-path-based semantic network analysis. Tian Z; Teng Z; Cheng S; Guo M BMC Syst Biol; 2018 Dec; 12(Suppl 9):134. PubMed ID: 30598084 [TBL] [Abstract][Full Text] [Related]
17. Inferring new indications for approved drugs via random walk on drug-disease heterogenous networks. Liu H; Song Y; Guan J; Luo L; Zhuang Z BMC Bioinformatics; 2016 Dec; 17(Suppl 17):539. PubMed ID: 28155639 [TBL] [Abstract][Full Text] [Related]
18. A comparative benchmarking and evaluation framework for heterogeneous network-based drug repositioning methods. Li Y; Yang Y; Tong Z; Wang Y; Mi Q; Bai M; Liang G; Li B; Shu K Brief Bioinform; 2024 Mar; 25(3):. PubMed ID: 38647153 [TBL] [Abstract][Full Text] [Related]
19. Computational drug repositioning using low-rank matrix approximation and randomized algorithms. Luo H; Li M; Wang S; Liu Q; Li Y; Wang J Bioinformatics; 2018 Jun; 34(11):1904-1912. PubMed ID: 29365057 [TBL] [Abstract][Full Text] [Related]
20. Drug repositioning by integrating target information through a heterogeneous network model. Wang W; Yang S; Zhang X; Li J Bioinformatics; 2014 Oct; 30(20):2923-30. PubMed ID: 24974205 [TBL] [Abstract][Full Text] [Related] [Next] [New Search]