A novel method for drug-target interaction prediction based on graph transformers model
| dc.contributor.author | Wang, Hongmei | |
| dc.contributor.author | Guo, Fang | |
| dc.contributor.author | Du, Mengyan | |
| dc.contributor.author | Wang, Guishen | |
| dc.contributor.author | Cao, Chen | |
| dc.date.accessioned | 2022-11-06T01:02:18Z | |
| dc.date.available | 2022-11-06T01:02:18Z | |
| dc.date.issued | 2022-11-03 | |
| dc.date.updated | 2022-11-06T01:02:17Z | |
| dc.description.abstract | Abstract Background Drug-target interactions (DTIs) prediction becomes more and more important for accelerating drug research and drug repositioning. Drug-target interaction network is a typical model for DTIs prediction. As many different types of relationships exist between drug and target, drug-target interaction network can be used for modeling drug-target interaction relationship. Recent works on drug-target interaction network are mostly concentrate on drug node or target node and neglecting the relationships between drug-target. Results We propose a novel prediction method for modeling the relationship between drug and target independently. Firstly, we use different level relationships of drugs and targets to construct feature of drug-target interaction. Then, we use line graph to model drug-target interaction. After that, we introduce graph transformer network to predict drug-target interaction. Conclusions This method introduces a line graph to model the relationship between drug and target. After transforming drug-target interactions from links to nodes, a graph transformer network is used to accomplish the task of predicting drug-target interactions. | |
| dc.identifier.citation | BMC Bioinformatics. 2022 Nov 03;23(1):459 | |
| dc.identifier.doi | https://doi.org/10.1186/s12859-022-04812-w | |
| dc.identifier.uri | http://hdl.handle.net/1880/115421 | |
| dc.identifier.uri | https://doi.org/10.11575/PRISM/44750 | |
| dc.language.rfc3066 | en | |
| dc.rights.holder | The Author(s) | |
| dc.title | A novel method for drug-target interaction prediction based on graph transformers model | |
| dc.type | Journal Article |