Abstract
MicroRNA (miRNA) is a non-coding RNA molecule whose length is about 22 nucleotides. The growing evidence shows that miRNA makes critical regulations in the development of complex diseases, such as cancers and cardiovascular diseases. Predicting potential miRNA-disease associations can provide a new perspective to achieve a better schemes of disease diagnosis and prognosis. However, predicting some potential essential miRNAs only with few known associations has proved challenging. Here we propose a novel method of probabilistic matrix decomposition for identifying miRNA-disease associations in heterogeneous omics data. First, we construct disease similarity network and miRNA similarity network to preprocess the miRNAs with none available associations. Then, we apply probabilistic factorization to obtain two feature matrices of miRNA and disease. Finally, we utilize obtained feature matrixes to identify potential associations for all diseases. The results indicate that PMDA is superior over other methods in predicting potential miRNA-disease associations in sparse and unbalance data. Moreover, we further estimate the performance of novel interactions in three typical diseases, and simulation results illustrate that PMDA could also achieve satisfactory predictive performance for novel diseases and miRNAs.
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Acknowledgements
This work was supported by Natural Science Foundation of China (Grant No. 61972141) and Natural Science Foundation of Hunan Province, China (Grant No. 2018JJ2053)
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He, K., Wu, R., Zhu, Z., Li, J., Lu, X. (2020). A Probabilistic Matrix Decomposition Method for Identifying miRNA-Disease Associations. In: Huang, DS., Jo, KH. (eds) Intelligent Computing Theories and Application. ICIC 2020. Lecture Notes in Computer Science(), vol 12464. Springer, Cham. https://doi.org/10.1007/978-3-030-60802-6_35
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DOI: https://doi.org/10.1007/978-3-030-60802-6_35
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