scholarly journals Drug-target interaction prediction: A Bayesian ranking approach

2017 ◽  
Vol 152 ◽  
pp. 15-21 ◽  
Author(s):  
Ladislav Peska ◽  
Krisztian Buza ◽  
Júlia Koller
Keyword(s):  
Drug Target ◽  
Target Interaction ◽  
Interaction Prediction ◽  
Bayesian Ranking

Drug-Target Interaction Prediction with Graph Regularized Matrix Factorization

2017 ◽  
Vol 14 (3) ◽  
pp. 646-656 ◽  
Author(s):  
Ali Ezzat ◽  
Peilin Zhao ◽  
Min Wu ◽  
Xiao-Li Li ◽  
Chee-Keong Kwoh
Keyword(s):  
Matrix Factorization ◽  
Drug Target ◽  
Target Interaction ◽  
Interaction Prediction

scholarly journals Autoencoders for Drug-Target Interaction Prediction

2020 ◽  
Author(s):  
Ming Chen ◽  
Xiuze Zhou
Keyword(s):  
Network Architecture ◽  
Drug Target ◽  
Real Data ◽  
Data Sets ◽  
Nonlinear Method ◽  
Target Interaction ◽  
Interaction Prediction ◽  
Biological Studies ◽  
Latent Features ◽  
Full Interaction

Abstract Background: Because it is so laborious and expensive to experimentally identify Drug-Target Interactions (DTIs), only a few DTIs have been verified. Computational methods are useful for identifying DTIs in biological studies of drug discovery and development. Results: For drug-target interaction prediction, we propose a novel neural network architecture, DAEi, extended from Denoising AutoEncoder (DAE). We assume that a set of verified DTIs is a corrupted version of the full interaction set. We use DAEi to learn latent features from corrupted DTIs to reconstruct the full input. Also, to better predict DTIs, we add some similarities to DAEi and adopt a new nonlinear method for calculation. Similarity information is very effective at improving the prediction of DTIs. Conclusion: Results of the extensive experiments we conducted on four real data sets show that our proposed methods are superior to other baseline approaches.Availability: All codes in this paper are open-sourced, and our projects are available at: https://github.com/XiuzeZhou/DAEi.

scholarly journals DrugE-Rank: improving drug–target interaction prediction of new candidate drugs or targets by ensemble learning to rank

2016 ◽  
Vol 32 (12) ◽  
pp. i18-i27 ◽  
Author(s):  
Qingjun Yuan ◽  
Junning Gao ◽  
Dongliang Wu ◽  
Shihua Zhang ◽  
Hiroshi Mamitsuka ◽  
...  
Keyword(s):  
Ensemble Learning ◽  
Drug Target ◽  
Learning To Rank ◽  
Target Interaction ◽  
Interaction Prediction
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