Identifying interactions between long non-coding RNAs (lncRNAs) and microRNAs (miRNAs) provides a new perspective for understanding regulatory relationships in plant life processes. Recently, computational methods based on graph neural networks (GNNs) have been widely employed to predict lncRNA-miRNA interactions (LMIs), which compensate for the inadequacy of biological experiments. However, the low-semantic and noise of graph limit the performance of existing GNN-based methods. In this paper, we develop a novel Counterfactual Heterogeneous Graph Attention Network (CFHAN) to improve the robust...
