Subgraph Topology and Dynamic Graph Topology Enhanced Graph Learning and Pairwise Feature Context Relationship Integration for Predicting Disease-Related miRNAs

作者：Ping Xuan, Xiaoying Qi, Sentao Chen, Jing Gu, Xiuju Wang, Hui Cui, Jun Lu, Tiangang Zhang · 发表于：Journal of Chemical Information and Modeling · 年份：2025 · DOI：10.1021/acs.jcim.4c01757 · 被引用次数：6 · 研究领域：MicroRNA in disease regulation、Circular RNAs in diseases、Cancer-related molecular mechanisms research

As an increasing number of microRNAs (miRNAs) have become biomarkers of various human diseases, prediction of the candidate disease-related miRNAs is helpful for facilitating the early diagnosis of diseases. Most of the recent prediction models concentrated on learning of the features from the heterogeneous graph composed of miRNAs and diseases. However, they failed to fully exploit the subgraph structures consisting of multiple miRNA and disease nodes, and they also did not completely integrate the context relationships among the pairwise features. We proposed a prediction model, SFPred, to integrate and encode the local topologies from neighborhood subgraphs, the dynamically evolved heterogeneous graph topology, and the context among pairwise features. First, the importance of an miRNA (disease) node to another node is formulated according to the subgraphs composed of their neighbors. Second, the features of each miRNA (disease) node continuously change when the graph encoding gradually deepens for the miRNA-disease heterogeneous network. A strategy based on multi-layer perceptron (MLP) is designed to estimate the edge weights according to the changed node features and form the dynamic graph topology. Third, considering the context relationships among the features of a pair of miRNA and disease nodes, a context relationship sensitive transformer is constructed to integrate these relationships. Finally, since the previous encoding layer of the transformer contains more detai...