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).

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 Drug-Target Interaction Prediction Based on Multisource Information Weighted Fusion

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Shuaiqi Liu ◽  
Jingjie An ◽  
Jie Zhao ◽  
Shuhuan Zhao ◽  
Hui Lv ◽  
...  
Keyword(s):  
Drug Target ◽  
Operating Characteristic ◽  
State Of The Art ◽  
Characteristic Curve ◽  
Relationship Matrix ◽  
Target Interaction ◽  
Interaction Prediction ◽  
Weighted Fusion ◽  
Precision Recall Curve ◽  
The Relationship

Recently, in most existing studies, it is assumed that there are no interaction relationships between drugs and targets with unknown interactions. However, unknown interactions mean the relationships between drugs and targets have just not been confirmed. In this paper, samples for which the relationship between drugs and targets has not been determined are considered unlabeled. A weighted fusion method of multisource information is proposed to screen drug-target interactions. Firstly, some drug-target pairs which may have interactions are selected. Secondly, the selected drug-target pairs are added to the positive samples, which are regarded as known to have interaction relationships, and the original interaction relationship matrix is revised. Finally, the revised datasets are used to predict the interaction derived from the bipartite local model with neighbor-based interaction profile inferring (BLM-NII). Experiments demonstrate that the proposed method has greatly improved specificity, sensitivity, precision, and accuracy compared with the BLM-NII method. In addition, compared with several state-of-the-art methods, the area under the receiver operating characteristic curve (AUC) and the area under the precision-recall curve (AUPR) of the proposed method are excellent.

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