321 related articles for article (PubMed ID: 28891684)
1. A Computational-Based Method for Predicting Drug-Target Interactions by Using Stacked Autoencoder Deep Neural Network.
Wang L; You ZH; Chen X; Xia SX; Liu F; Yan X; Zhou Y; Song KJ
J Comput Biol; 2018 Mar; 25(3):361-373. PubMed ID: 28891684
[TBL] [Abstract][Full Text] [Related]
2. Predicting drug-target interaction network using deep learning model.
You J; McLeod RD; Hu P
Comput Biol Chem; 2019 Jun; 80():90-101. PubMed ID: 30939415
[TBL] [Abstract][Full Text] [Related]
3. DeepStack-DTIs: Predicting Drug-Target Interactions Using LightGBM Feature Selection and Deep-Stacked Ensemble Classifier.
Zhang Y; Jiang Z; Chen C; Wei Q; Gu H; Yu B
Interdiscip Sci; 2022 Jun; 14(2):311-330. PubMed ID: 34731411
[TBL] [Abstract][Full Text] [Related]
4. 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]
5. 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]
6. An efficient computational method for predicting drug-target interactions using weighted extreme learning machine and speed up robot features.
An JY; Meng FR; Yan ZJ
BioData Min; 2021 Jan; 14(1):3. PubMed ID: 33472664
[TBL] [Abstract][Full Text] [Related]
7. Drug-Target Interaction Prediction Based on Drug Fingerprint Information and Protein Sequence.
Li Y; Huang YA; You ZH; Li LP; Wang Z
Molecules; 2019 Aug; 24(16):. PubMed ID: 31430892
[TBL] [Abstract][Full Text] [Related]
8. Prediction of Drug-Target Interactions by Combining Dual-Tree Complex Wavelet Transform with Ensemble Learning Method.
Pan J; Li LP; You ZH; Yu CQ; Ren ZH; Chen Y
Molecules; 2021 Sep; 26(17):. PubMed ID: 34500792
[TBL] [Abstract][Full Text] [Related]
9. 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]
10. Predicting Drug-Target Interactions Based on Small Positive Samples.
Hu P; Chan KCC; Hu Y
Curr Protein Pept Sci; 2018; 19(5):479-487. PubMed ID: 27829343
[TBL] [Abstract][Full Text] [Related]
11. Incorporating chemical sub-structures and protein evolutionary information for inferring drug-target interactions.
Wang L; You ZH; Li LP; Yan X; Zhang W
Sci Rep; 2020 Apr; 10(1):6641. PubMed ID: 32313024
[TBL] [Abstract][Full Text] [Related]
12. Ensemble Learning Prediction of Drug-Target Interactions Using GIST Descriptor Extracted from PSSM-Based Evolutionary Information.
Zhan X; You Z; Yu C; Li L; Pan J
Biomed Res Int; 2020; 2020():4516250. PubMed ID: 32908888
[TBL] [Abstract][Full Text] [Related]
13. RFDT: A Rotation Forest-based Predictor for Predicting Drug-Target Interactions Using Drug Structure and Protein Sequence Information.
Wang L; You ZH; Chen X; Yan X; Liu G; Zhang W
Curr Protein Pept Sci; 2018; 19(5):445-454. PubMed ID: 27842479
[TBL] [Abstract][Full Text] [Related]
14. 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]
15. 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]
16. Computational Prediction of DrugTarget Interactions Using Chemical, Biological, and Network Features.
Cao DS; Zhang LX; Tan GS; Xiang Z; Zeng WB; Xu QS; Chen AF
Mol Inform; 2014 Oct; 33(10):669-81. PubMed ID: 27485302
[TBL] [Abstract][Full Text] [Related]
17. MSPEDTI: Prediction of Drug-Target Interactions via Molecular Structure with Protein Evolutionary Information.
Wang L; Wong L; Chen ZH; Hu J; Sun XF; Li Y; You ZH
Biology (Basel); 2022 May; 11(5):. PubMed ID: 35625468
[TBL] [Abstract][Full Text] [Related]
18. A Novel Approach for Drug-Target Interactions Prediction Based on Multimodal Deep Autoencoder.
Wang H; Wang J; Dong C; Lian Y; Liu D; Yan Z
Front Pharmacol; 2019; 10():1592. PubMed ID: 32047432
[TBL] [Abstract][Full Text] [Related]
19. 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]
20. Predicting protein-protein interactions from protein sequences by a stacked sparse autoencoder deep neural network.
Wang YB; You ZH; Li X; Jiang TH; Chen X; Zhou X; Wang L
Mol Biosyst; 2017 Jun; 13(7):1336-1344. PubMed ID: 28604872
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
