Predicting disease genes based on multi-head attention fusion
Abstract Background The identification of disease-related genes is of great significance for the diagnosis and treatment of human disease. Most studies have focused on developing efficient and accurate computational methods to predict disease-causing genes. Due to the sparsity and complexity of biom...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2023-04-01
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| Series: | BMC Bioinformatics |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12859-023-05285-1 |
| _version_ | 1797840815171567616 |
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| author | Linlin Zhang Dianrong Lu Xuehua Bi Kai Zhao Guanglei Yu Na Quan |
| author_facet | Linlin Zhang Dianrong Lu Xuehua Bi Kai Zhao Guanglei Yu Na Quan |
| author_sort | Linlin Zhang |
| collection | DOAJ |
| description | Abstract Background The identification of disease-related genes is of great significance for the diagnosis and treatment of human disease. Most studies have focused on developing efficient and accurate computational methods to predict disease-causing genes. Due to the sparsity and complexity of biomedical data, it is still a challenge to develop an effective multi-feature fusion model to identify disease genes. Results This paper proposes an approach to predict the pathogenic gene based on multi-head attention fusion (MHAGP). Firstly, the heterogeneous biological information networks of disease genes are constructed by integrating multiple biomedical knowledge databases. Secondly, two graph representation learning algorithms are used to capture the feature vectors of gene-disease pairs from the network, and the features are fused by introducing multi-head attention. Finally, multi-layer perceptron model is used to predict the gene-disease association. Conclusions The MHAGP model outperforms all of other methods in comparative experiments. Case studies also show that MHAGP is able to predict genes potentially associated with diseases. In the future, more biological entity association data, such as gene-drug, disease phenotype-gene ontology and so on, can be added to expand the information in heterogeneous biological networks and achieve more accurate predictions. In addition, MHAGP with strong expansibility can be used for potential tasks such as gene-drug association and drug-disease association prediction. |
| first_indexed | 2024-04-09T16:20:49Z |
| format | Article |
| id | doaj.art-c66c264b015441c28c315f25e6775223 |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-04-09T16:20:49Z |
| publishDate | 2023-04-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-c66c264b015441c28c315f25e67752232023-04-23T11:30:00ZengBMCBMC Bioinformatics1471-21052023-04-0124111510.1186/s12859-023-05285-1Predicting disease genes based on multi-head attention fusionLinlin Zhang0Dianrong Lu1Xuehua Bi2Kai Zhao3Guanglei Yu4Na Quan5College of Software Engineering, Xinjiang UniversityCollege of information Science and Engineering, Xinjiang UniversityMedical Engineering and Technology College, Xinjiang Medical UniversityCollege of information Science and Engineering, Xinjiang UniversityMedical Engineering and Technology College, Xinjiang Medical UniversityCollege of information Science and Engineering, Xinjiang UniversityAbstract Background The identification of disease-related genes is of great significance for the diagnosis and treatment of human disease. Most studies have focused on developing efficient and accurate computational methods to predict disease-causing genes. Due to the sparsity and complexity of biomedical data, it is still a challenge to develop an effective multi-feature fusion model to identify disease genes. Results This paper proposes an approach to predict the pathogenic gene based on multi-head attention fusion (MHAGP). Firstly, the heterogeneous biological information networks of disease genes are constructed by integrating multiple biomedical knowledge databases. Secondly, two graph representation learning algorithms are used to capture the feature vectors of gene-disease pairs from the network, and the features are fused by introducing multi-head attention. Finally, multi-layer perceptron model is used to predict the gene-disease association. Conclusions The MHAGP model outperforms all of other methods in comparative experiments. Case studies also show that MHAGP is able to predict genes potentially associated with diseases. In the future, more biological entity association data, such as gene-drug, disease phenotype-gene ontology and so on, can be added to expand the information in heterogeneous biological networks and achieve more accurate predictions. In addition, MHAGP with strong expansibility can be used for potential tasks such as gene-drug association and drug-disease association prediction.https://doi.org/10.1186/s12859-023-05285-1Pathogenic gene predictionHeterogeneous networkMulti-head attentionGraph representation learning |
| spellingShingle | Linlin Zhang Dianrong Lu Xuehua Bi Kai Zhao Guanglei Yu Na Quan Predicting disease genes based on multi-head attention fusion BMC Bioinformatics Pathogenic gene prediction Heterogeneous network Multi-head attention Graph representation learning |
| title | Predicting disease genes based on multi-head attention fusion |
| title_full | Predicting disease genes based on multi-head attention fusion |
| title_fullStr | Predicting disease genes based on multi-head attention fusion |
| title_full_unstemmed | Predicting disease genes based on multi-head attention fusion |
| title_short | Predicting disease genes based on multi-head attention fusion |
| title_sort | predicting disease genes based on multi head attention fusion |
| topic | Pathogenic gene prediction Heterogeneous network Multi-head attention Graph representation learning |
| url | https://doi.org/10.1186/s12859-023-05285-1 |
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