Abstract
MicroRNAs (miRNAs) play a vital role in regulating various cellular processes, and involving the occurrence of various complex diseases. The association prediction between miRNAs and diseases provides a reference for exploration of the underlying pathogenesis of diseases. Some published prediction methods cleverly alleviate the inherent noise and incompleteness of biological data sets, and greatly improve the accuracy of prediction, but these methods still have room for optimization. In this research, we presented a novel method called MELPMDA, which is based on matrix enhancement and label propagation to infer the potential association between miRNAs and diseases. In order to enhance the most reliable similarity information, we established a similarity reward matrix based on three cases of strong connection, weak connection and negative connection. Then, a self-adjusting method was constructed to extract effective similarity information, which can enhance the association matrix to reduce its sparsity. In addition, label propagation was utilized as predictive model to further discover unobvious associations. Finally, the AUC obtained by 5-fold Cross-Validation (5CV) was 0.9550, which proved the rationality and effectiveness of our method. Furthermore, the predictive reliability of MELPMDA was further validated by the positive results in a case study of hepatocellular carcinoma.
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Acknowledgements
This work was supported by the National Natural Science Foundation of China (Nos. U19A2064, 61873001).
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Zhang, ZW., Gao, Z., Zheng, CH., Wang, YT., Qi, SM. (2021). MELPMDA: A New Method Based on Matrix Enhancement and Label Propagation for Predicting miRNA-Disease Association. In: Huang, DS., Jo, KH., Li, J., Gribova, V., Premaratne, P. (eds) Intelligent Computing Theories and Application. ICIC 2021. Lecture Notes in Computer Science(), vol 12838. Springer, Cham. https://doi.org/10.1007/978-3-030-84532-2_48
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DOI: https://doi.org/10.1007/978-3-030-84532-2_48
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