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. 2023 Apr 21;24(1):162.
doi: 10.1186/s12859-023-05285-1.

Predicting disease genes based on multi-head attention fusion

Linlin Zhang  1 , Dianrong Lu  2 , Xuehua Bi  3 , Kai Zhao  2 , Guanglei Yu  3 , Na Quan  2
Affiliations

Predicting disease genes based on multi-head attention fusion

Linlin Zhang et al. BMC Bioinformatics. .

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.

Keywords: Graph representation learning; Heterogeneous network; Multi-head attention; Pathogenic gene prediction.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
MHAGP framework. A Three heterogeneous networks are constructed based on the four integrated data sources (gene, disease, lncRNA and miRNA) and seven kinds of association (disease-miRNA, gene-miRNA, gene functional similarity, gene-disease, semantic similarity of disease, gene-lncRNA, disease-lncRNA). B The Node2vec and LINE algorithms are used to mine the biological association features of genes and diseases from three heterogeneous networks. The features extracted from the GMD and GLD networks are used to fusion the gene-disease association features in GD networks by multi-head attention. C Self-attention is introduced to predict the pathogenic gene in the multi-layer perceptron and output the gene-disease association score
Fig. 2
Fig. 2
Dimension e-value comparison result
Fig. 3
Fig. 3
ROC curve for different value of five-fold cross-validation
Fig. 4
Fig. 4
Accuracy of the model based on feature combinations
See this image and copyright information in PMC

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