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. 2019 Jun 24;20(1):353.
doi: 10.1186/s12859-019-2956-5.

NPCMF: Nearest Profile-based Collaborative Matrix Factorization method for predicting miRNA-disease associations

Ying-Lian Gao  1 , Zhen Cui  2 , Jin-Xing Liu  3   4 , Juan Wang  2 , Chun-Hou Zheng  5
Affiliations

NPCMF: Nearest Profile-based Collaborative Matrix Factorization method for predicting miRNA-disease associations

Ying-Lian Gao et al. BMC Bioinformatics. .

Abstract

Background: Predicting meaningful miRNA-disease associations (MDAs) is costly. Therefore, an increasing number of researchers are beginning to focus on methods to predict potential MDAs. Thus, prediction methods with improved accuracy are under development. An efficient computational method is proposed to be crucial for predicting novel MDAs. For improved experimental productivity, large biological datasets are used by researchers. Although there are many effective and feasible methods to predict potential MDAs, the possibility remains that these methods are flawed.

Results: A simple and effective method, known as Nearest Profile-based Collaborative Matrix Factorization (NPCMF), is proposed to identify novel MDAs. The nearest profile is introduced to our method to achieve the highest AUC value compared with other advanced methods. For some miRNAs and diseases without any association, we use the nearest neighbour information to complete the prediction.

Conclusions: To evaluate the performance of our method, five-fold cross-validation is used to calculate the AUC value. At the same time, three disease cases, gastric neoplasms, rectal neoplasms and colonic neoplasms, are used to predict novel MDAs on a gold-standard dataset. We predict the vast majority of known MDAs and some novel MDAs. Finally, the prediction accuracy of our method is determined to be better than that of other existing methods. Thus, the proposed prediction model can obtain reliable experimental results.

Keywords: Gaussian interaction profile; Matrix factorization; MiRNA-disease association prediction; Nearest profile.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
Comparison of convergence about NPCMF and CMF. Compared with the CMF, the NPCMF converges the fastest
Fig. 2
Fig. 2
The ROC curve for each method in a 5-fold cross validation experiment
Fig. 3
Fig. 3
Sensitivity analysis for K under CV-p
Fig. 4
Fig. 4
Sensitivity analysis for p under CV-p
Fig. 5
Fig. 5
Sensitivity analysis for α under CV-p
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