Abstract
MiRNA is a kind of RNA that cannot be translated into protein and is a kind of non-coding RNA molecules. In the current research, miRNA is a hot topic: many variants and dysregulations of miRNA are closely related to human diseases and they participate in the occurrence of various diseases. Predicting and confirming disease-related miRNAs helps to understand the pathogenesis of disease at miRNA molecular level. Matrix completion methods are often used to predict potential associations between miRNAs and diseases. In the methods, we want to fill in the missing entries of the matrix based on the small portion of entries observed. Thus, we developed a new approach for the prediction of miRNA–disease associations by using inductive matrix completion (named MGAPG) of an accelerated proximal gradient algorithm in this work. To evaluate the performance of MGAPG, a comparison between the MGAPG and other algorithms reveals the reliable performance of MGAPG which performed well in a 5-fold cross-validation. The AUC values of MGAPG are higher than some well-known methods, indicating its outstanding performance. Moreover, in the case of colon cancer associated with miRNA-diseases known in the DBDEMC, HMDD and Mir2disease databases, 90% of the top 20 predictive miRNA were confirmed by the experimental report. These results fully demonstrate the reliability of MGAPG predictive ability.
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Acknowledgement
This work was supported by the grants of the National Science Foundation of China (Grant Nos. 61472467 and 61672011) and the National Key R&D Program of China (2017YFC1311003). And i would like to thank Yingting Jiang for providing help in grammar and spelling.
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Mao, G., Wang, SL. (2019). A Novel Approach to Predicting MiRNA-Disease Associations. In: Huang, DS., Jo, KH., Huang, ZK. (eds) Intelligent Computing Theories and Application. ICIC 2019. Lecture Notes in Computer Science(), vol 11644. Springer, Cham. https://doi.org/10.1007/978-3-030-26969-2_34
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DOI: https://doi.org/10.1007/978-3-030-26969-2_34
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