MCCMF: collaborative matrix factorization based on matrix completion for predicting miRNA-disease associations
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METHODOLOGY ARTICLE
Open Access
MCCMF: collaborative matrix factorization based on matrix completion for predicting miRNA‑disease associations Tian‑Ru Wu, Meng‑Meng Yin, Cui‑Na Jiao, Ying‑Lian Gao, Xiang‑Zhen Kong and Jin‑Xing Liu*
*Correspondence: [email protected] School of Computer Science, Qufu Normal University, Rizhao 276826, China
Abstract Background: MicroRNAs (miRNAs) are non-coding RNAs with regulatory functions. Many studies have shown that miRNAs are closely associated with human diseases. Among the methods to explore the relationship between the miRNA and the disease, traditional methods are time-consuming and the accuracy needs to be improved. In view of the shortcoming of previous models, a method, collaborative matrix factori‑ zation based on matrix completion (MCCMF) is proposed to predict the unknown miRNA-disease associations. Results: The complete matrix of the miRNA and the disease is obtained by matrix completion. Moreover, Gaussian Interaction Profile kernel is added to the miRNA functional similarity matrix and the disease semantic similarity matrix. Then the Weight K Nearest Known Neighbors method is used to pretreat the association matrix, so the model is close to the reality. Finally, collaborative matrix factorization method is applied to obtain the prediction results. Therefore, the MCCMF obtains a satisfactory result in the fivefold cross-validation, with an AUC of 0.9569 (0.0005). Conclusions: The AUC value of MCCMF is higher than other advanced methods in the fivefold cross validation experiment. In order to comprehensively evaluate the performance of MCCMF, accuracy, precision, recall and f-measure are also added. The final experimental results demonstrate that MCCMF outperforms other methods in predicting miRNA-disease associations. In the end, the effectiveness and practicability of MCCMF are further verified by researching three specific diseases. Keywords: MiRNA-disease association prediction, Matrix completion, Weight K Nearest Known Neighbors, Matrix factorization Tian-Ru Wu: Mail: [email protected]
Background MicroRNAs (MiRNAs) are a class of non-coding single-stranded RNA molecules. Their lengths are usually 18–24 nucleotides. Instead of synthesizing proteins, miRNAs participate in post-transcriptional regulation of gene expression in eukaryotes and viruses [1]. In spite of the first miRNA Line-4 was discovered in 1993 [2], the diversity and © The Author(s) 2020. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the mate‑ rial. If material is not included in the article’s Creative Commons licence and your i
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