Abstract
MicroRNAs (miRNAs) are crucial regulators of gene expression, including in diseases such as cancer. Although machine learning methods have shown promise in predicting miRNA-target interactions, they encounter challenges related to imbalanced classes and false positives. To tackle these issues, this study proposes a GNN-based model, using a variant of GraphSAGE algorithm named HinSAGE, which integrates validated miRNA-mRNA and mRNA-mRNA interactions with cancer-related gene expression data. Results show that our approach effectively learns miRNA-target interaction patterns from the graph structure and node features. The model achieves 77% precision, 80% recall, 78% F1-score, and 86% ROC AUC on the test data. It competes well with related approaches, reaching an F1-score of approximately 90% on a common test set. Thus, GNNs offer a promising avenue for studying miRNA-target interactions, providing balanced predictive power and improved precision through negative interaction sampling from the graph.
This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001, and by grants from FAPERGS [21/2551-0002052-0] and CNPq [308075/2021-8].
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Vianna Fabiano, E.A., Recamonde-Mendoza, M. (2023). Prediction of Cancer-Related miRNA Targets Using an Integrative Heterogeneous Graph Neural Network-Based Method. In: Naldi, M.C., Bianchi, R.A.C. (eds) Intelligent Systems. BRACIS 2023. Lecture Notes in Computer Science(), vol 14197. Springer, Cham. https://doi.org/10.1007/978-3-031-45392-2_23
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