123 related articles for article (PubMed ID: 38849874)
21. GSL-DTI: Graph structure learning network for Drug-Target interaction prediction.
E Z; Qiao G; Wang G; Li Y
Methods; 2024 Mar; 223():136-145. PubMed ID: 38360082
[TBL] [Abstract][Full Text] [Related]
22. 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]
23. MDL-CPI: Multi-view deep learning model for compound-protein interaction prediction.
Wei L; Long W; Wei L
Methods; 2022 Aug; 204():418-427. PubMed ID: 35114401
[TBL] [Abstract][Full Text] [Related]
24. ALDPI: adaptively learning importance of multi-scale topologies and multi-modality similarities for drug-protein interaction prediction.
Hu K; Cui H; Zhang T; Sun C; Xuan P
Brief Bioinform; 2022 Mar; 23(2):. PubMed ID: 35108362
[TBL] [Abstract][Full Text] [Related]
25. A novel candidate disease gene prioritization method using deep graph convolutional networks and semi-supervised learning.
Azadifar S; Ahmadi A
BMC Bioinformatics; 2022 Oct; 23(1):422. PubMed ID: 36241966
[TBL] [Abstract][Full Text] [Related]
26. A novel hybrid framework for metabolic pathways prediction based on the graph attention network.
Yang Z; Liu J; Shah HA; Feng J
BMC Bioinformatics; 2022 Sep; 23(Suppl 5):329. PubMed ID: 36171550
[TBL] [Abstract][Full Text] [Related]
27. A deep learning method for predicting molecular properties and compound-protein interactions.
Ma J; Zhang R; Li T; Jiang J; Zhao Z; Liu Y; Ma J
J Mol Graph Model; 2022 Dec; 117():108283. PubMed ID: 35994925
[TBL] [Abstract][Full Text] [Related]
28. GVDTI: graph convolutional and variational autoencoders with attribute-level attention for drug-protein interaction prediction.
Xuan P; Fan M; Cui H; Zhang T; Nakaguchi T
Brief Bioinform; 2022 Jan; 23(1):. PubMed ID: 34718408
[TBL] [Abstract][Full Text] [Related]
29. Pre-training graph neural networks for link prediction in biomedical networks.
Long Y; Wu M; Liu Y; Fang Y; Kwoh CK; Chen J; Luo J; Li X
Bioinformatics; 2022 Apr; 38(8):2254-2262. PubMed ID: 35171981
[TBL] [Abstract][Full Text] [Related]
30. ParaCPI: A Parallel Graph Convolutional Network for Compound-Protein Interaction Prediction.
Zhang L; Zeng W; Chen J; Chen J; Li K
IEEE/ACM Trans Comput Biol Bioinform; 2024 May; PP():. PubMed ID: 38787671
[TBL] [Abstract][Full Text] [Related]
31. Drug-target interaction predication via multi-channel graph neural networks.
Li Y; Qiao G; Wang K; Wang G
Brief Bioinform; 2022 Jan; 23(1):. PubMed ID: 34661237
[TBL] [Abstract][Full Text] [Related]
32. CAT-CPI: Combining CNN and transformer to learn compound image features for predicting compound-protein interactions.
Qian Y; Wu J; Zhang Q
Front Mol Biosci; 2022; 9():963912. PubMed ID: 36188230
[TBL] [Abstract][Full Text] [Related]
33. A Biological Feature and Heterogeneous Network Representation Learning-Based Framework for Drug-Target Interaction Prediction.
Liu L; Zhang Q; Wei Y; Zhao Q; Liao B
Molecules; 2023 Sep; 28(18):. PubMed ID: 37764321
[TBL] [Abstract][Full Text] [Related]
34. Identifying drug-target interactions based on graph convolutional network and deep neural network.
Zhao T; Hu Y; Valsdottir LR; Zang T; Peng J
Brief Bioinform; 2021 Mar; 22(2):2141-2150. PubMed ID: 32367110
[TBL] [Abstract][Full Text] [Related]
35. Co-embedding of edges and nodes with deep graph convolutional neural networks.
Zhou Y; Huo H; Hou Z; Bu L; Mao J; Wang Y; Lv X; Bu F
Sci Rep; 2023 Oct; 13(1):16966. PubMed ID: 37807013
[TBL] [Abstract][Full Text] [Related]
36. GraphCPIs: A novel graph-based computational model for potential compound-protein interactions.
Chen ZH; Zhao BW; Li JQ; Guo ZH; You ZH
Mol Ther Nucleic Acids; 2023 Jun; 32():721-728. PubMed ID: 37251691
[TBL] [Abstract][Full Text] [Related]
37. A Novel Method to Predict Drug-Target Interactions Based on Large-Scale Graph Representation Learning.
Zhao BW; You ZH; Hu L; Guo ZH; Wang L; Chen ZH; Wong L
Cancers (Basel); 2021 Apr; 13(9):. PubMed ID: 33925568
[TBL] [Abstract][Full Text] [Related]
38. Enhancing generalizability and performance in drug-target interaction identification by integrating pharmacophore and pre-trained models.
Zhang Z; He X; Long D; Luo G; Chen S
Bioinformatics; 2024 Jun; 40(Supplement_1):i539-i547. PubMed ID: 38940179
[TBL] [Abstract][Full Text] [Related]
39. 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]
40. GCFMCL: predicting miRNA-drug sensitivity using graph collaborative filtering and multi-view contrastive learning.
Wei J; Zhuo L; Zhou Z; Lian X; Fu X; Yao X
Brief Bioinform; 2023 Jul; 24(4):. PubMed ID: 37427977
[TBL] [Abstract][Full Text] [Related]
[Previous] [Next] [New Search]