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 NeuRank: learning to rank with neural networks for drug–target interaction prediction

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Xiujin Wu ◽  
Wenhua Zeng ◽  
Fan Lin ◽  
Xiuze Zhou
Keyword(s):  
Network Architecture ◽  
Drug Target ◽  
Learning To Rank ◽  
Data Sets ◽  
Neural Network Architecture ◽  
Ranking Problem ◽  
Target Interaction ◽  
Similar Drug ◽  
Public Data ◽  
G Protein Coupled

Abstract Background Experimental verification of a drug discovery process is expensive and time-consuming. Therefore, recently, the demand to more efficiently and effectively identify drug–target interactions (DTIs) has intensified. Results We treat the prediction of DTIs as a ranking problem and propose a neural network architecture, NeuRank, to address it. Also, we assume that similar drug compounds are likely to interact with similar target proteins. Thus, in our model, we add drug and target similarities, which are very effective at improving the prediction of DTIs. Then, we develop NeuRank from a point-wise to a pair-wise, and further to list-wise model. Conclusion Finally, results from extensive experiments on five public data sets (DrugBank, Enzymes, Ion Channels, G-Protein-Coupled Receptors, and Nuclear Receptors) show that, in identifying DTIs, our models achieve better performance than other state-of-the-art methods.

scholarly journals Cascade Deep Forest With Heterogeneous Similarity Measures for Drug–Target Interaction Prediction

2021 ◽  
Vol 12 ◽  
Author(s):  
Ying Zheng ◽  
Zheng Wu
Keyword(s):  
Drug Target ◽  
Drug Repositioning ◽  
Similarity Measures ◽  
Data Sets ◽  
Target Interaction ◽  
Interaction Prediction ◽  
Deep Forest ◽  
Network Properties ◽  
Precision Recall Curve ◽  
Fold Cross Validation

Drug repositioning is a method of systematically identifying potential molecular targets that known drugs may act on. Compared with traditional methods, drug repositioning has been extensively studied due to the development of multi-omics technology and system biology methods. Because of its biological network properties, it is possible to apply machine learning related algorithms for prediction. Based on various heterogeneous network model, this paper proposes a method named THNCDF for predicting drug–target interactions. Various heterogeneous networks are integrated to build a tripartite network, and similarity calculation methods are used to obtain similarity matrix. Then, the cascade deep forest method is used to make prediction. Results indicate that THNCDF outperforms the previously reported methods based on the 10-fold cross-validation on the benchmark data sets proposed by Y. Yamanishi. The area under Precision Recall curve (AUPR) value on the Enzyme, GPCR, Ion Channel, and Nuclear Receptor data sets is 0.988, 0.980, 0.938, and 0.906 separately. The experimental results well illustrate the feasibility of this method.

The Computational Models of Drug-target Interaction Prediction

2020 ◽  
Vol 27 (5) ◽  
pp. 348-358 ◽  
Author(s):  
Yijie Ding ◽  
Jijun Tang ◽  
Fei Guo
Keyword(s):  
Machine Learning ◽  
Computational Methods ◽  
Drug Target ◽  
Computational Models ◽  
Data Sets ◽  
Target Interaction ◽  
Interaction Prediction ◽  
The Past ◽  
Benchmark Data ◽  
Precision Recall Curve

:The identification of Drug-Target Interactions (DTIs) is an important process in drug discovery and medical research. However, the tradition experimental methods for DTIs identification are still time consuming, extremely expensive and challenging. In the past ten years, various computational methods have been developed to identify potential DTIs. In this paper, the identification methods of DTIs are summarized. What's more, several state-of-the-art computational methods are mainly introduced, containing network-based method and machine learning-based method. In particular, for machine learning-based methods, including the supervised and semisupervised models, have essential differences in the approach of negative samples. Although these effective computational models in identification of DTIs have achieved significant improvements, network-based and machine learning-based methods have their disadvantages, respectively. These computational methods are evaluated on four benchmark data sets via values of Area Under the Precision Recall curve (AUPR).

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
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