Node-adaptive graph Transformer with structural encoding for accurate and robust lncRNA-disease association prediction.
BMC Genomics
; 25(1): 73, 2024 Jan 18.
Article
em En
| MEDLINE
| ID: mdl-38233788
ABSTRACT
BACKGROUND:
Long noncoding RNAs (lncRNAs) are integral to a plethora of critical cellular biological processes, including the regulation of gene expression, cell differentiation, and the development of tumors and cancers. Predicting the relationships between lncRNAs and diseases can contribute to a better understanding of the pathogenic mechanisms of disease and provide strong support for the development of advanced treatment methods.RESULTS:
Therefore, we present an innovative Node-Adaptive Graph Transformer model for predicting unknown LncRNA-Disease Associations, named NAGTLDA. First, we utilize the node-adaptive feature smoothing (NAFS) method to learn the local feature information of nodes and encode the structural information of the fusion similarity network of diseases and lncRNAs using Structural Deep Network Embedding (SDNE). Next, the Transformer module is used to capture potential association information between the network nodes. Finally, we employ a Transformer module with two multi-headed attention layers for learning global-level embedding fusion. Network structure coding is added as the structural inductive bias of the network to compensate for the missing message-passing mechanism in Transformer. NAGTLDA achieved an average AUC of 0.9531 and AUPR of 0.9537 significantly higher than state-of-the-art methods in 5-fold cross validation. We perform case studies on 4 diseases; 55 out of 60 associations between lncRNAs and diseases have been validated in the literatures. The results demonstrate the enormous potential of the graph Transformer structure to incorporate graph structural information for uncovering lncRNA-disease unknown correlations.CONCLUSIONS:
Our proposed NAGTLDA model can serve as a highly efficient computational method for predicting biological information associations.Palavras-chave
Texto completo:
1
Bases de dados:
MEDLINE
Assunto principal:
RNA Longo não Codificante
/
Neoplasias
Tipo de estudo:
Prognostic_studies
/
Risk_factors_studies
Limite:
Humans
Idioma:
En
Revista:
BMC Genomics
Assunto da revista:
GENETICA
Ano de publicação:
2024
Tipo de documento:
Article
País de afiliação:
China