MEAHNE: miRNA-Disease Association Prediction Based on Semantic Information in a Heterogeneous Network

Correct prediction of potential miRNA-disease pairs can considerably accelerate the experimental process in biomedical research. However, many methods cannot effectively learn the complex information contained in multisource data, limiting the performance of the prediction model. A heterogeneous network prediction model (MEAHNE) is proposed to make full use of the complex information contained in multisource data. To fully mine the potential relationship between miRNA and disease, we collected multisource data and constructed a heterogeneous network. After constructing the network, we mined potential associations in the network through a designed heterogeneous network framework (MEAHNE). MEAHNE first learned the semantic information of the metapath instances, then used the attention mechanism to encode the semantic information as attention weights and aggregated nodes of the same type using the attention weights. The semantic information was also integrated into the node. MEAHNE optimized parameters through end-to-end training. MEAHNE was compared with other state-of-the-art heterogeneous graph neural network methods. The values of the area under the precision-recall curve and the receiver operating characteristic curve demonstrated the superiority of MEAHNE. In addition, MEAHNE predicted 20 miRNAs each for breast cancer and nasopharyngeal cancer and verified 18 miRNAs related to breast cancer and 14 miRNAs related to nasopharyngeal cancer by consulting related databases.