SEBGLMA: Semantic Embedded Bipartite Graph Network for Predicting lncRNA-miRNA Associations

Abstract

Identifying the association between long noncoding RNA (lncRNA) and micro-RNA (miRNA) is of great significance for the treatment of diseases by interfering with the combination of miRNA and messenger RNA (mRNA). Although many efforts and resources have been invested to identify lncRNA-miRNA associations (LMAs), clinical trials are still expensive and laborious. Nevertheless, the experiments also need to consult a large number of side effects. Therefore, novel computer-aided models are urgently needed to predict LMAs. This paper proposed a semantic embedded bipartite graph network for predicting lncRNA-miRNA associations (SEBGLMA), which provided a novel feature extraction method by integrating K-mer segmentation, word2vec, Gaussian interaction profile (GIP), and graph convolution network (GCN). Concretely, the attribute characteristics of RNA sequences are extracted by K-mer segmentation and word2vec modules. Afterward, the adjacent matrix is completed by GIP self-similarity. Then, the attribute characteristics and adjacent matrix are fed into GCN for embedding behavior features. Finally, the features are sent into the rotation forest (RoF) for detecting potential LMAs. The average accuracy, precision, sensitivity, specificity, Matthews correlation coefficient, and F1-Score are 87.09%, 87.66%, 87.03%, 87.84%, 74.18%, and 86.99% on the benchmark data set. For fairly validating the performance of our model, we conducted various comparisons with different classifiers. Furthermore, the case studies of hsa-miR-497-5P and NONHSAT022145.2 are also established. The results of comparisons and case studies further illustrated that our method is anticipated to become a robust and reliable tool for the identification of LMAs.

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