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RSCMDA: Prediction of Potential miRNA–Disease Associations Based on a Robust Similarity Constraint Learning Method

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Abstract

With the rapid development of biotechnology and computer technology, increasing studies have shown that the occurrence of many diseases in the human body is closely related to the dysfunction of miRNA, and the relationship between them has become a new research hotspot. Exploring disease-related miRNAs information provides a new perspective for understanding the etiology and pathogenesis of diseases. In this study, we proposed a new method based on similarity constrained learning (RSCMDA) to infer disease-associated miRNAs. Considering the problems of noise and incomplete information in current biological datasets, we designed a new framework RSCMDA, which can learn a new disease similarity network and miRNA similarity network based on the existing biological information, and then update the predicted miRNA–disease associations using robust similarity constraint learning method. Consequently, the AUC scores obtained in the global and local cross-validation of RSCMDA are 0.9465 and 0.8494, respectively, which are superior to the other methods. Besides, the prediction performance of RSCMDA is further confirmed by the case study on lung Neoplasms, because 94% of the top 50 miRNAs predicted by the RSCMDA method are confirmed from the existing biological databases or research results. All the results show that RSCMDA is a reliable and effective framework, which can be used as new technology to explore the relationship between miRNA and disease.

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Acknowledgements

This work was supported by the National Science Foundation of China (No. 61672329, 62072290) in part by the Project of the Shandong Provincial Project of Education Scientific Plan (No. SDYY18058).

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  1. School of Information Science and Engineering, Shandong Normal University, Jinan, 250358, China

    Yu ShengPeng & Wang Hong

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ShengPeng, Y., Hong, W. RSCMDA: Prediction of Potential miRNA–Disease Associations Based on a Robust Similarity Constraint Learning Method. Interdiscip Sci Comput Life Sci 13, 559–571 (2021). https://doi.org/10.1007/s12539-021-00459-y

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