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BMC Bioinformatics. 2019 Dec 24;20(Suppl 25):686. doi: 10.1186/s12859-019-3260-0.

RCMF: a robust collaborative matrix factorization method to predict miRNA-disease associations.

Author information

1
School of Information Science and Engineering, Qufu Normal University, Rizhao, 276826, China.
2
School of Information Science and Engineering, Qufu Normal University, Rizhao, 276826, China. sdcavell@126.com.
3
Co-Innovation Center for Information Supply & Assurance Technology, Anhui University, Hefei, 230601, China. sdcavell@126.com.
4
Qufu Normal University Library, Qufu Normal University, Rizhao, 276826, China.
5
Co-Innovation Center for Information Supply & Assurance Technology, Anhui University, Hefei, 230601, China.
6
School of Information Science and Engineering, Qufu Normal University, Rizhao, 276826, China. wangjuansdu@163.com.

Abstract

BACKGROUND:

Predicting miRNA-disease associations (MDAs) is time-consuming and expensive. It is imminent to improve the accuracy of prediction results. So it is crucial to develop a novel computing technology to predict new MDAs. Although some existing methods can effectively predict novel MDAs, there are still some shortcomings. Especially when the disease matrix is processed, its sparsity is an important factor affecting the final results.

RESULTS:

A robust collaborative matrix factorization (RCMF) is proposed to predict novel MDAs. The L2,1-norm are introduced to our method to achieve the highest AUC value than other advanced methods.

CONCLUSIONS:

5-fold cross validation is used to evaluate our method, and simulation experiments are used to predict novel associations on Gold Standard Dataset. Finally, our prediction accuracy is better than other existing advanced methods. Therefore, our approach is effective and feasible in predicting novel MDAs.

KEYWORDS:

Collaborative regularization; L2,1-norm; Matrix factorization; MiRNA-disease association prediction

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