A Novel Approach for Drug-Target Interactions Prediction Based on Multimodal Deep Autoencoder
Drug targets are biomacromolecules or biomolecular structures that bind to specific drugs and produce therapeutic effects. Therefore, the prediction of drug-target interactions (DTIs) is important for disease therapy. Incorporating multiple similarity measures for drugs and targets is of essence for...
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Frontiers Media S.A.
2020-01-01
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| Series: | Frontiers in Pharmacology |
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| Online Access: | https://www.frontiersin.org/article/10.3389/fphar.2019.01592/full |
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doaj-013e6c2226c644f1b232ec1c410fbce72020-11-24T22:02:23ZengFrontiers Media S.A.Frontiers in Pharmacology1663-98122020-01-011010.3389/fphar.2019.01592478955A Novel Approach for Drug-Target Interactions Prediction Based on Multimodal Deep AutoencoderHuiqing Wang0Jingjing Wang1Chunlin Dong2Yuanyuan Lian3Dan Liu4Zhiliang Yan5College of Information and Computer, Taiyuan University of Technology, Taiyuan, ChinaCollege of Information and Computer, Taiyuan University of Technology, Taiyuan, ChinaDryland Agriculture Research Center, Shanxi Academy of Agricultural Sciences, Taiyuan, ChinaCollege of Information and Computer, Taiyuan University of Technology, Taiyuan, ChinaCollege of Information and Computer, Taiyuan University of Technology, Taiyuan, ChinaCollege of Information and Computer, Taiyuan University of Technology, Taiyuan, ChinaDrug targets are biomacromolecules or biomolecular structures that bind to specific drugs and produce therapeutic effects. Therefore, the prediction of drug-target interactions (DTIs) is important for disease therapy. Incorporating multiple similarity measures for drugs and targets is of essence for improving the accuracy of prediction of DTIs. However, existing studies with multiple similarity measures ignored the global structure information of similarity measures, and required manual extraction features of drug-target pairs, ignoring the non-linear relationship among features. In this paper, we proposed a novel approach MDADTI for DTIs prediction based on MDA. MDADTI applied random walk with restart method and positive pointwise mutual information to calculate the topological similarity matrices of drugs and targets, capturing the global structure information of similarity measures. Then, MDADTI applied multimodal deep autoencoder to fuse multiple topological similarity matrices of drugs and targets, automatically learned the low-dimensional features of drugs and targets, and applied deep neural network to predict DTIs. The results of 5-repeats of 10-fold cross-validation under three different cross-validation settings indicated that MDADTI is superior to the other four baseline methods. In addition, we validated the predictions of the MDADTI in six drug-target interactions reference databases, and the results showed that MDADTI can effectively identify unknown DTIs.https://www.frontiersin.org/article/10.3389/fphar.2019.01592/fulldrug-target interactionsmultiple similarity measuresrandom walk with restartpositive pointwise mutual informationmultimodal deep autoencoder |
| collection |
DOAJ |
| language |
English |
| format |
Article |
| sources |
DOAJ |
| author |
Huiqing Wang Jingjing Wang Chunlin Dong Yuanyuan Lian Dan Liu Zhiliang Yan |
| spellingShingle |
Huiqing Wang Jingjing Wang Chunlin Dong Yuanyuan Lian Dan Liu Zhiliang Yan A Novel Approach for Drug-Target Interactions Prediction Based on Multimodal Deep Autoencoder Frontiers in Pharmacology drug-target interactions multiple similarity measures random walk with restart positive pointwise mutual information multimodal deep autoencoder |
| author_facet |
Huiqing Wang Jingjing Wang Chunlin Dong Yuanyuan Lian Dan Liu Zhiliang Yan |
| author_sort |
Huiqing Wang |
| title |
A Novel Approach for Drug-Target Interactions Prediction Based on Multimodal Deep Autoencoder |
| title_short |
A Novel Approach for Drug-Target Interactions Prediction Based on Multimodal Deep Autoencoder |
| title_full |
A Novel Approach for Drug-Target Interactions Prediction Based on Multimodal Deep Autoencoder |
| title_fullStr |
A Novel Approach for Drug-Target Interactions Prediction Based on Multimodal Deep Autoencoder |
| title_full_unstemmed |
A Novel Approach for Drug-Target Interactions Prediction Based on Multimodal Deep Autoencoder |
| title_sort |
novel approach for drug-target interactions prediction based on multimodal deep autoencoder |
| publisher |
Frontiers Media S.A. |
| series |
Frontiers in Pharmacology |
| issn |
1663-9812 |
| publishDate |
2020-01-01 |
| description |
Drug targets are biomacromolecules or biomolecular structures that bind to specific drugs and produce therapeutic effects. Therefore, the prediction of drug-target interactions (DTIs) is important for disease therapy. Incorporating multiple similarity measures for drugs and targets is of essence for improving the accuracy of prediction of DTIs. However, existing studies with multiple similarity measures ignored the global structure information of similarity measures, and required manual extraction features of drug-target pairs, ignoring the non-linear relationship among features. In this paper, we proposed a novel approach MDADTI for DTIs prediction based on MDA. MDADTI applied random walk with restart method and positive pointwise mutual information to calculate the topological similarity matrices of drugs and targets, capturing the global structure information of similarity measures. Then, MDADTI applied multimodal deep autoencoder to fuse multiple topological similarity matrices of drugs and targets, automatically learned the low-dimensional features of drugs and targets, and applied deep neural network to predict DTIs. The results of 5-repeats of 10-fold cross-validation under three different cross-validation settings indicated that MDADTI is superior to the other four baseline methods. In addition, we validated the predictions of the MDADTI in six drug-target interactions reference databases, and the results showed that MDADTI can effectively identify unknown DTIs. |
| topic |
drug-target interactions multiple similarity measures random walk with restart positive pointwise mutual information multimodal deep autoencoder |
| url |
https://www.frontiersin.org/article/10.3389/fphar.2019.01592/full |
| work_keys_str_mv |
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