161 related articles for article (PubMed ID: 35625468)
21. DeepACTION: A deep learning-based method for predicting novel drug-target interactions.
Hasan Mahmud SM; Chen W; Jahan H; Dai B; Din SU; Dzisoo AM
Anal Biochem; 2020 Dec; 610():113978. PubMed ID: 33035462
[TBL] [Abstract][Full Text] [Related]
22. Semi-supervised heterogeneous graph contrastive learning for drug-target interaction prediction.
Yao K; Wang X; Li W; Zhu H; Jiang Y; Li Y; Tian T; Yang Z; Liu Q; Liu Q
Comput Biol Med; 2023 Sep; 163():107199. PubMed ID: 37421738
[TBL] [Abstract][Full Text] [Related]
23. EDC-DTI: An end-to-end deep collaborative learning model based on multiple information for drug-target interactions prediction.
Yuan Y; Zhang Y; Meng X; Liu Z; Wang B; Miao R; Zhang R; Su W; Liu L
J Mol Graph Model; 2023 Jul; 122():108498. PubMed ID: 37126908
[TBL] [Abstract][Full Text] [Related]
24. A deep learning-based method for drug-target interaction prediction based on long short-term memory neural network.
Wang YB; You ZH; Yang S; Yi HC; Chen ZH; Zheng K
BMC Med Inform Decis Mak; 2020 Mar; 20(Suppl 2):49. PubMed ID: 32183788
[TBL] [Abstract][Full Text] [Related]
25. Potential SARS-CoV-2 nonstructural proteins inhibitors: drugs repurposing with drug-target networks and deep learning.
Azmoodeh SK; Tsigelny IF; Kouznetsova VL
Front Biosci (Landmark Ed); 2022 Apr; 27(4):113. PubMed ID: 35468672
[TBL] [Abstract][Full Text] [Related]
26. Comparative analysis of network-based approaches and machine learning algorithms for predicting drug-target interactions.
Jung YS; Kim Y; Cho YR
Methods; 2022 Feb; 198():19-31. PubMed ID: 34737033
[TBL] [Abstract][Full Text] [Related]
27. MHADTI: predicting drug-target interactions via multiview heterogeneous information network embedding with hierarchical attention mechanisms.
Tian Z; Peng X; Fang H; Zhang W; Dai Q; Ye Y
Brief Bioinform; 2022 Nov; 23(6):. PubMed ID: 36242566
[TBL] [Abstract][Full Text] [Related]
28. A learning-based method for drug-target interaction prediction based on feature representation learning and deep neural network.
Peng J; Li J; Shang X
BMC Bioinformatics; 2020 Sep; 21(Suppl 13):394. PubMed ID: 32938374
[TBL] [Abstract][Full Text] [Related]
29. Predicting drug-target interactions using Lasso with random forest based on evolutionary information and chemical structure.
Shi H; Liu S; Chen J; Li X; Ma Q; Yu B
Genomics; 2019 Dec; 111(6):1839-1852. PubMed ID: 30550813
[TBL] [Abstract][Full Text] [Related]
30. An Ensemble Learning-Based Method for Inferring Drug-Target Interactions Combining Protein Sequences and Drug Fingerprints.
Zhao ZY; Huang WZ; Zhan XK; Pan J; Huang YA; Zhang SW; Yu CQ
Biomed Res Int; 2021; 2021():9933873. PubMed ID: 33987446
[TBL] [Abstract][Full Text] [Related]
31. 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]
32. DeepConv-DTI: Prediction of drug-target interactions via deep learning with convolution on protein sequences.
Lee I; Keum J; Nam H
PLoS Comput Biol; 2019 Jun; 15(6):e1007129. PubMed ID: 31199797
[TBL] [Abstract][Full Text] [Related]
33. DeepFusion: A deep learning based multi-scale feature fusion method for predicting drug-target interactions.
Song T; Zhang X; Ding M; Rodriguez-Paton A; Wang S; Wang G
Methods; 2022 Aug; 204():269-277. PubMed ID: 35219861
[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. Improved prediction of drug-target interactions based on ensemble learning with fuzzy local ternary pattern.
Zhao ZY; Huang WZ; Zhan XK; Huang YA; Zhang SW; Yu CQ
Front Biosci (Landmark Ed); 2021 Jul; 26(7):222-234. PubMed ID: 34340269
[No Abstract] [Full Text] [Related]
36. DTI-CDF: a cascade deep forest model towards the prediction of drug-target interactions based on hybrid features.
Chu Y; Kaushik AC; Wang X; Wang W; Zhang Y; Shan X; Salahub DR; Xiong Y; Wei DQ
Brief Bioinform; 2021 Jan; 22(1):451-462. PubMed ID: 31885041
[TBL] [Abstract][Full Text] [Related]
37. Predicting drug-target interactions from drug structure and protein sequence using novel convolutional neural networks.
Hu S; Zhang C; Chen P; Gu P; Zhang J; Wang B
BMC Bioinformatics; 2019 Dec; 20(Suppl 25):689. PubMed ID: 31874614
[TBL] [Abstract][Full Text] [Related]
38. Inferring Drug-Target Interactions Based on Random Walk and Convolutional Neural Network.
Xu X; Xuan P; Zhang T; Chen B; Sheng N
IEEE/ACM Trans Comput Biol Bioinform; 2022; 19(4):2294-2304. PubMed ID: 33729947
[TBL] [Abstract][Full Text] [Related]
39. DLDTI: a learning-based framework for drug-target interaction identification using neural networks and network representation.
Zhao Y; Zheng K; Guan B; Guo M; Song L; Gao J; Qu H; Wang Y; Shi D; Zhang Y
J Transl Med; 2020 Nov; 18(1):434. PubMed ID: 33187537
[TBL] [Abstract][Full Text] [Related]
40. IMCHGAN: Inductive Matrix Completion With Heterogeneous Graph Attention Networks for Drug-Target Interactions Prediction.
Li J; Wang J; Lv H; Zhang Z; Wang Z
IEEE/ACM Trans Comput Biol Bioinform; 2022; 19(2):655-665. PubMed ID: 34115592
[TBL] [Abstract][Full Text] [Related]
[Previous] [Next] [New Search]
