DTiGNN: Learning drug-target embedding from a heterogeneous biological network based on a two-level attention-based graph neural network
Motivation: In vitro experiment-based drug-target interaction (DTI) exploration demands more human, financial and data resources. In silico approaches have been recommended for predicting DTIs to reduce time and cost. During the drug development process, one can analyze the therapeutic effect of the...
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| Format: | Article |
| Language: | English |
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AIMS Press
2023-03-01
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| Series: | Mathematical Biosciences and Engineering |
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| Online Access: | https://www.aimspress.com/article/doi/10.3934/mbe.2023419?viewType=HTML |
| _version_ | 1827965589816082432 |
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| author | Saranya Muniyappan Arockia Xavier Annie Rayan Geetha Thekkumpurath Varrieth |
| author_facet | Saranya Muniyappan Arockia Xavier Annie Rayan Geetha Thekkumpurath Varrieth |
| author_sort | Saranya Muniyappan |
| collection | DOAJ |
| description | Motivation: In vitro experiment-based drug-target interaction (DTI) exploration demands more human, financial and data resources. In silico approaches have been recommended for predicting DTIs to reduce time and cost. During the drug development process, one can analyze the therapeutic effect of the drug for a particular disease by identifying how the drug binds to the target for treating that disease. Hence, DTI plays a major role in drug discovery. Many computational methods have been developed for DTI prediction. However, the existing methods have limitations in terms of capturing the interactions via multiple semantics between drug and target nodes in a heterogeneous biological network (HBN). Methods: In this paper, we propose a DTiGNN framework for identifying unknown drug-target pairs. The DTiGNN first calculates the similarity between the drug and target from multiple perspectives. Then, the features of drugs and targets from each perspective are learned separately by using a novel method termed an information entropy-based random walk. Next, all of the learned features from different perspectives are integrated into a single drug and target similarity network by using a multi-view convolutional neural network. Using the integrated similarity networks, drug interactions, drug-disease associations, protein interactions and protein-disease association, the HBN is constructed. Next, a novel embedding algorithm called a meta-graph guided graph neural network is used to learn the embedding of drugs and targets. Then, a convolutional neural network is employed to infer new DTIs after balancing the sample using oversampling techniques. Results: The DTiGNN is applied to various datasets, and the result shows better performance in terms of the area under receiver operating characteristic curve (AUC) and area under precision-recall curve (AUPR), with scores of 0.98 and 0.99, respectively. There are 23,739 newly predicted DTI pairs in total. |
| first_indexed | 2024-04-09T17:35:33Z |
| format | Article |
| id | doaj.art-f7e2634fcc924c438dced4673ee14a14 |
| institution | Directory Open Access Journal |
| issn | 1551-0018 |
| language | English |
| last_indexed | 2024-04-09T17:35:33Z |
| publishDate | 2023-03-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | Mathematical Biosciences and Engineering |
| spelling | doaj.art-f7e2634fcc924c438dced4673ee14a142023-04-18T02:04:19ZengAIMS PressMathematical Biosciences and Engineering1551-00182023-03-012059530957110.3934/mbe.2023419DTiGNN: Learning drug-target embedding from a heterogeneous biological network based on a two-level attention-based graph neural networkSaranya Muniyappan 0Arockia Xavier Annie Rayan1 Geetha Thekkumpurath Varrieth2Computer Science and Engineering, CEG Campus, Anna University, Tamil Nadu, IndiaComputer Science and Engineering, CEG Campus, Anna University, Tamil Nadu, IndiaComputer Science and Engineering, CEG Campus, Anna University, Tamil Nadu, IndiaMotivation: In vitro experiment-based drug-target interaction (DTI) exploration demands more human, financial and data resources. In silico approaches have been recommended for predicting DTIs to reduce time and cost. During the drug development process, one can analyze the therapeutic effect of the drug for a particular disease by identifying how the drug binds to the target for treating that disease. Hence, DTI plays a major role in drug discovery. Many computational methods have been developed for DTI prediction. However, the existing methods have limitations in terms of capturing the interactions via multiple semantics between drug and target nodes in a heterogeneous biological network (HBN). Methods: In this paper, we propose a DTiGNN framework for identifying unknown drug-target pairs. The DTiGNN first calculates the similarity between the drug and target from multiple perspectives. Then, the features of drugs and targets from each perspective are learned separately by using a novel method termed an information entropy-based random walk. Next, all of the learned features from different perspectives are integrated into a single drug and target similarity network by using a multi-view convolutional neural network. Using the integrated similarity networks, drug interactions, drug-disease associations, protein interactions and protein-disease association, the HBN is constructed. Next, a novel embedding algorithm called a meta-graph guided graph neural network is used to learn the embedding of drugs and targets. Then, a convolutional neural network is employed to infer new DTIs after balancing the sample using oversampling techniques. Results: The DTiGNN is applied to various datasets, and the result shows better performance in terms of the area under receiver operating characteristic curve (AUC) and area under precision-recall curve (AUPR), with scores of 0.98 and 0.99, respectively. There are 23,739 newly predicted DTI pairs in total.https://www.aimspress.com/article/doi/10.3934/mbe.2023419?viewType=HTMLdrug-target interaction predictionsimilarity network integrationinformation entropy-based random walkmulti-view convolutional neural networkmeta-graph-based representation learninggraph neural network |
| spellingShingle | Saranya Muniyappan Arockia Xavier Annie Rayan Geetha Thekkumpurath Varrieth DTiGNN: Learning drug-target embedding from a heterogeneous biological network based on a two-level attention-based graph neural network Mathematical Biosciences and Engineering drug-target interaction prediction similarity network integration information entropy-based random walk multi-view convolutional neural network meta-graph-based representation learning graph neural network |
| title | DTiGNN: Learning drug-target embedding from a heterogeneous biological network based on a two-level attention-based graph neural network |
| title_full | DTiGNN: Learning drug-target embedding from a heterogeneous biological network based on a two-level attention-based graph neural network |
| title_fullStr | DTiGNN: Learning drug-target embedding from a heterogeneous biological network based on a two-level attention-based graph neural network |
| title_full_unstemmed | DTiGNN: Learning drug-target embedding from a heterogeneous biological network based on a two-level attention-based graph neural network |
| title_short | DTiGNN: Learning drug-target embedding from a heterogeneous biological network based on a two-level attention-based graph neural network |
| title_sort | dtignn learning drug target embedding from a heterogeneous biological network based on a two level attention based graph neural network |
| topic | drug-target interaction prediction similarity network integration information entropy-based random walk multi-view convolutional neural network meta-graph-based representation learning graph neural network |
| url | https://www.aimspress.com/article/doi/10.3934/mbe.2023419?viewType=HTML |
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