Mining disease-associated genes based on heterogeneous graph transformer

Identification of disease-associated genes is a crucial step in unraveling the underlying molecular mechanisms of complex diseases. As a potent computational tool for recognizing potential associations between genes and diseases, heterogeneous graphs allow for more comprehensive and accurate modeling and analysis of intricate molecular interactions within unified networks. In this study, a method based on Heterogeneous Graph Transformer (HGT) is proposed for mining associations between genes and diseases from heterogeneous graphs. Six types of association data are collected among four biological components: Protein-coding (PC) genes, long non-coding RNAs (lncRNAs), microRNAs, and diseases. A series of processing and integration steps are performed on these raw data to construct an extensive heterogeneous graph. For the vertexes in the graph, their description texts are collected and specialized natural language processing model, BioBERT, is employed to generate semantic vectors as the initial feature vectors for the vertexes. The topological structure of this heterogeneous graph along with the nodes' initial features collectively serve as input to the HGT, enabling information aggregation. Finally, the gene and disease vector representations produced by HGT are used to compute the association scores, which are further applied in the association prediction. Experimental results demonstrate that the proposed method outperforms existing state-of-the-art methods in terms of accuracy and exhibits a remarkably high degree of reliability. Moreover, the method has the capability to implicitly capture meaningful biological mechanisms through learned implicit meta-paths, which can offer substantial guidance for subsequent biomedical validations.