Abstract
MiRNAs play an important role in the occurrence and development of human disease. Identifying potential miRNA-disease associations is valuable for disease diagnosis and treatment. Therefore, it is very urgent to develop efficient computational methods for predicting potential miRNA-disease associations in order to reduce the cost and time associated with biological wet experiments. In addition, although the good performance achieved by graph neural network methods for predicting miRNA-disease associations, they still face the risk of over-smoothing and have room for improvement. In this paper, we propose a novel model named nSGC-MDA, which employs a modified Simple Graph Convolution (SGC) to predict the miRNA-disease associations. Specifically, we first construct a bipartite attributed graph for miRNAs and diseases by computing multi-source similarity. Then we adapt SGC to extract the features of miRNAs and diseases on the graph. To prevent over-fitting, we randomly drop the message during message propagation and employ Jumping Knowledge (JK) during feature aggregation to enhance feature representation. Furthermore, we utilize a feature crossing strategy to get the feature of miRNA-disease pairs. Finally, we calculate the prediction scores of miRNA-disease pairs by using a fully connected neural network decoder. In the five-fold cross-validation, nSGC-MDA achieves a mean AUC of 0.9502 and a mean AUPR of 0.9496, outperforming six compared methods. The case study of cardiovascular disease also demonstrates the effectiveness of nSGC-MDA.
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Acknowledgements
This work was supported by the National Natural Science Foundation of China (Nos. 12061071, 61962050), the Key R &D Program of Xinjiang Uygur Autonomous Region (No. 2022B03023), the Natural Science Foundation of Xinjiang Uygur Autonomous Region (Nos. 2022D01C427, 2022D01C429 and 2020D01C028), the Natural Science Foundation of Hunan Province of China (No. 2022JJ30428). This work was carried out in part using computing resources at the High Performance Computing Center of Central South University.
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Bi, X., Jiang, C., Yan, C., Zhao, K., Zhang, L., Wang, J. (2023). Identifying miRNA-Disease Associations Based on Simple Graph Convolution with DropMessage and Jumping Knowledge. In: Guo, X., Mangul, S., Patterson, M., Zelikovsky, A. (eds) Bioinformatics Research and Applications. ISBRA 2023. Lecture Notes in Computer Science(), vol 14248. Springer, Singapore. https://doi.org/10.1007/978-981-99-7074-2_4
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DOI: https://doi.org/10.1007/978-981-99-7074-2_4
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