Drug-Gene Interaction Prediction Method through Cross Granularity Subgraph Contrastive Learning and Attention Mechanism Fusion
[Purposes] Clarifying the interconnections between drugs and genes is an important topic in drug development. At present, the graph neural network method based on the random walk algorithm has achieved great results in identifying drug-gene interaction relationships. However, existing methods with s...
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| Format: | Article |
| Language: | English |
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Editorial Office of Journal of Taiyuan University of Technology
2025-01-01
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| Series: | Taiyuan Ligong Daxue xuebao |
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| Online Access: | https://tyutjournal.tyut.edu.cn/englishpaper/show-2372.html |
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| author | HU Dongdong PENG Yang TAN Shuqiu ZHU Xiaofei |
| author_facet | HU Dongdong PENG Yang TAN Shuqiu ZHU Xiaofei |
| author_sort | HU Dongdong |
| collection | DOAJ |
| description | [Purposes] Clarifying the interconnections between drugs and genes is an important topic in drug development. At present, the graph neural network method based on the random walk algorithm has achieved great results in identifying drug-gene interaction relationships. However, existing methods with single graph neural network modeling can’t aggregate the information of neighbor nodes well. In addition, most methods use a simple way for the node representation of drugs and genes fusing, which fais to effectively use the information represented by nodes for the classification of interaction relationships. To address the above issues, a cross granularity contrastive learning and attention fusion method is proposed for predicting drug-gene interaction relationships. [Methods] On one hand, a cross granularity contrastive learning method is adopted to obtain node information from both distant and close distances. On the other hand, by utilizing attention fusion mechanisms, hidden information in nodes can be fully mined, and attention fusion can be performed on distance information. [Findings] The experimental results on two real datasets show that compared with the baseline; the proposed model has better classification performance. |
| format | Article |
| id | doaj-art-66ace9e702254843937b2763d4672ea4 |
| institution | Kabale University |
| issn | 1007-9432 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Editorial Office of Journal of Taiyuan University of Technology |
| record_format | Article |
| series | Taiyuan Ligong Daxue xuebao |
| spelling | doaj-art-66ace9e702254843937b2763d4672ea42025-02-12T03:34:22ZengEditorial Office of Journal of Taiyuan University of TechnologyTaiyuan Ligong Daxue xuebao1007-94322025-01-0156112713610.16355/j.tyut.1007-9432.202308221007-9432(2025)01-0127-10Drug-Gene Interaction Prediction Method through Cross Granularity Subgraph Contrastive Learning and Attention Mechanism FusionHU Dongdong0PENG Yang1TAN Shuqiu2ZHU Xiaofei3College of Computer Science and Engineering, Chongqing University of Technology, Chongqing, ChinaCollege of Computer Science and Engineering, Chongqing University of Technology, Chongqing, ChinaCollege of Computer Science and Engineering, Chongqing University of Technology, Chongqing, ChinaCollege of Computer Science and Engineering, Chongqing University of Technology, Chongqing, China[Purposes] Clarifying the interconnections between drugs and genes is an important topic in drug development. At present, the graph neural network method based on the random walk algorithm has achieved great results in identifying drug-gene interaction relationships. However, existing methods with single graph neural network modeling can’t aggregate the information of neighbor nodes well. In addition, most methods use a simple way for the node representation of drugs and genes fusing, which fais to effectively use the information represented by nodes for the classification of interaction relationships. To address the above issues, a cross granularity contrastive learning and attention fusion method is proposed for predicting drug-gene interaction relationships. [Methods] On one hand, a cross granularity contrastive learning method is adopted to obtain node information from both distant and close distances. On the other hand, by utilizing attention fusion mechanisms, hidden information in nodes can be fully mined, and attention fusion can be performed on distance information. [Findings] The experimental results on two real datasets show that compared with the baseline; the proposed model has better classification performance.https://tyutjournal.tyut.edu.cn/englishpaper/show-2372.htmlcontrastive learninggraph representation learningrelational graph neural networkattention mechanismgene-drug interaction prediction |
| spellingShingle | HU Dongdong PENG Yang TAN Shuqiu ZHU Xiaofei Drug-Gene Interaction Prediction Method through Cross Granularity Subgraph Contrastive Learning and Attention Mechanism Fusion Taiyuan Ligong Daxue xuebao contrastive learning graph representation learning relational graph neural network attention mechanism gene-drug interaction prediction |
| title | Drug-Gene Interaction Prediction Method through Cross Granularity Subgraph Contrastive Learning and Attention Mechanism Fusion |
| title_full | Drug-Gene Interaction Prediction Method through Cross Granularity Subgraph Contrastive Learning and Attention Mechanism Fusion |
| title_fullStr | Drug-Gene Interaction Prediction Method through Cross Granularity Subgraph Contrastive Learning and Attention Mechanism Fusion |
| title_full_unstemmed | Drug-Gene Interaction Prediction Method through Cross Granularity Subgraph Contrastive Learning and Attention Mechanism Fusion |
| title_short | Drug-Gene Interaction Prediction Method through Cross Granularity Subgraph Contrastive Learning and Attention Mechanism Fusion |
| title_sort | drug gene interaction prediction method through cross granularity subgraph contrastive learning and attention mechanism fusion |
| topic | contrastive learning graph representation learning relational graph neural network attention mechanism gene-drug interaction prediction |
| url | https://tyutjournal.tyut.edu.cn/englishpaper/show-2372.html |
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