229 related articles for article (PubMed ID: 32657406)
1. Combining phenome-driven drug-target interaction prediction with patients' electronic health records-based clinical corroboration toward drug discovery.
Zhou M; Zheng C; Xu R
Bioinformatics; 2020 Jul; 36(Suppl_1):i436-i444. PubMed ID: 32657406
[TBL] [Abstract][Full Text] [Related]
2. A Drug-Side Effect Context-Sensitive Network approach for drug target prediction.
Zhou M; Chen Y; Xu R
Bioinformatics; 2019 Jun; 35(12):2100-2107. PubMed ID: 30428013
[TBL] [Abstract][Full Text] [Related]
3. Phenome-driven disease genetics prediction toward drug discovery.
Chen Y; Li L; Zhang GQ; Xu R
Bioinformatics; 2015 Jun; 31(12):i276-83. PubMed ID: 26072493
[TBL] [Abstract][Full Text] [Related]
4. Context-sensitive network-based disease genetics prediction and its implications in drug discovery.
Chen Y; Xu R
Bioinformatics; 2017 Apr; 33(7):1031-1039. PubMed ID: 28062449
[TBL] [Abstract][Full Text] [Related]
5. Predicting drug-target interactions using restricted Boltzmann machines.
Wang Y; Zeng J
Bioinformatics; 2013 Jul; 29(13):i126-34. PubMed ID: 23812976
[TBL] [Abstract][Full Text] [Related]
6. Prediction and evaluation of combination pharmacotherapy using natural language processing, machine learning and patient electronic health records.
Ding P; Pan Y; Wang Q; Xu R
J Biomed Inform; 2022 Sep; 133():104164. PubMed ID: 35985621
[TBL] [Abstract][Full Text] [Related]
7. NMTF-DTI: A Nonnegative Matrix Tri-factorization Approach With Multiple Kernel Fusion for Drug-Target Interaction Prediction.
Jamali AA; Kusalik A; Wu FX
IEEE/ACM Trans Comput Biol Bioinform; 2023; 20(1):586-594. PubMed ID: 34914594
[TBL] [Abstract][Full Text] [Related]
8. MultiDTI: drug-target interaction prediction based on multi-modal representation learning to bridge the gap between new chemical entities and known heterogeneous network.
Zhou D; Xu Z; Li W; Xie X; Peng S
Bioinformatics; 2021 Dec; 37(23):4485-4492. PubMed ID: 34180970
[TBL] [Abstract][Full Text] [Related]
9. A Machine Learning Approach for Drug-target Interaction Prediction using Wrapper Feature Selection and Class Balancing.
Redkar S; Mondal S; Joseph A; Hareesha KS
Mol Inform; 2020 May; 39(5):e1900062. PubMed ID: 32003548
[TBL] [Abstract][Full Text] [Related]
10. Multiple similarity drug-target interaction prediction with random walks and matrix factorization.
Liu B; Papadopoulos D; Malliaros FD; Tsoumakas G; Papadopoulos AN
Brief Bioinform; 2022 Sep; 23(5):. PubMed ID: 36070659
[TBL] [Abstract][Full Text] [Related]
11. Mining comorbidities of opioid use disorder from FDA adverse event reporting system and patient electronic health records.
Pan Y; Xu R
BMC Med Inform Decis Mak; 2022 Jun; 22(Suppl 2):155. PubMed ID: 35710401
[TBL] [Abstract][Full Text] [Related]
12. Inferring Interactions between Novel Drugs and Novel Targets via Instance-Neighborhood-Based Models.
Shi JY; Li JX; Chen BL; Zhang Y
Curr Protein Pept Sci; 2018; 19(5):488-497. PubMed ID: 27829347
[TBL] [Abstract][Full Text] [Related]
13. SDTNBI: an integrated network and chemoinformatics tool for systematic prediction of drug-target interactions and drug repositioning.
Wu Z; Cheng F; Li J; Li W; Liu G; Tang Y
Brief Bioinform; 2017 Mar; 18(2):333-347. PubMed ID: 26944082
[TBL] [Abstract][Full Text] [Related]
14. Disease comorbidity-guided drug repositioning: a case study in schizophrenia.
Wang Q; Xu R
AMIA Annu Symp Proc; 2018; 2018():1300-1309. PubMed ID: 30815174
[TBL] [Abstract][Full Text] [Related]
15. An end-to-end heterogeneous graph representation learning-based framework for drug-target interaction prediction.
Peng J; Wang Y; Guan J; Li J; Han R; Hao J; Wei Z; Shang X
Brief Bioinform; 2021 Sep; 22(5):. PubMed ID: 33517357
[TBL] [Abstract][Full Text] [Related]
16. DTI-Voodoo: machine learning over interaction networks and ontology-based background knowledge predicts drug-target interactions.
Hinnerichs T; Hoehndorf R
Bioinformatics; 2021 Dec; 37(24):4835-4843. PubMed ID: 34320178
[TBL] [Abstract][Full Text] [Related]
17. Drug repurposing for opioid use disorders: integration of computational prediction, clinical corroboration, and mechanism of action analyses.
Zhou M; Wang Q; Zheng C; John Rush A; Volkow ND; Xu R
Mol Psychiatry; 2021 Sep; 26(9):5286-5296. PubMed ID: 33432189
[TBL] [Abstract][Full Text] [Related]
18. Improved prediction of drug-target interactions using regularized least squares integrating with kernel fusion technique.
Hao M; Wang Y; Bryant SH
Anal Chim Acta; 2016 Feb; 909():41-50. PubMed ID: 26851083
[TBL] [Abstract][Full Text] [Related]
19. DASPfind: new efficient method to predict drug-target interactions.
Ba-Alawi W; Soufan O; Essack M; Kalnis P; Bajic VB
J Cheminform; 2016; 8():15. PubMed ID: 26985240
[TBL] [Abstract][Full Text] [Related]
20. 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]
[Next] [New Search]
