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A non-negative matrix factorization based method for predicting disease-associated miRNAs in miRNA-disease bilayer
Yingli Zhong1, Ping Xuan1, Xiao Wang2
1School of Computer Science and Technology, Heilongjiang University, Harbin, China.
Motivation:
Identification of disease-associated miRNAs (disease miRNAs) is critical for understanding disease etiology and pathogenesis. Since miRNAs exert their functions by regulating the expression of their target mRNAs, several methods based on the target genes were proposed to predict disease miRNA candidates. They achieved only limited success as they all suffered from the high false-positive rate of target prediction results. Alternatively, other prediction methods were based on the observation that miRNAs with similar functions tend to be associated with similar diseases and vice versa. The methods exploited the information about miRNAs and diseases, including the functional similarities between miRNAs, the similarities between diseases, and the associations between miRNAs and diseases. However, how to integrate the multiple kinds of information completely and consider the biological characteristic of disease miRNAs is a challenging problem.
Results:
We constructed a bilayer network to represent the complex relationships among miRNAs, among diseases and between miRNAs and diseases. We proposed a non-negative matrix factorization based method to rank, so as to predict, the disease miRNA candidates. The method integrated the miRNA functional similarity, the disease similarity and the miRNA-disease associations seamlessly, which exploited the complex relationships within the bilayer network and the consensus relationship between multiple kinds of information. Considering the correlation between the candidates related to various diseases, it predicted their respective candidates for all the diseases simultaneously. In addition, the sparseness characteristic of disease miRNAs was introduced to generate more reliable prediction model that excludes those noisy candidates. The results on 15 common diseases showed a superior performance of the new method for not only well-characterized diseases but also new ones. A detailed case study on breast neoplasms, colorectal neoplasms, lung neoplasms and 32 other diseases demonstrated the ability of the method for discovering potential disease miRNAs.
Availability And Implementation:
The web service for the new method and the list of predicted candidates for all the diseases are available at http://www.bioinfolab.top.
Supplementary Information:
Supplementary data are available at Bioinformatics online.
Insights
This study introduces a novel method to identify disease-associated microRNAs (miRNAs) by integrating miRNA and disease similarities. The approach effectively predicts potential disease miRNAs, improving upon existing methods.
Area of Science:
- Bioinformatics
- Genomics
- Computational Biology
Background:
- Identifying disease-associated microRNAs (miRNAs) is crucial for understanding disease mechanisms.
- Existing methods for predicting disease miRNAs often suffer from high false-positive rates due to reliance on target gene prediction.
- Integrating diverse information like miRNA function, disease similarity, and known miRNA-disease associations presents a significant challenge.
Purpose of the Study:
- To develop a robust method for predicting disease-associated miRNA candidates.
- To overcome limitations of existing prediction approaches by integrating multiple data types.
- To improve the accuracy and reliability of disease miRNA identification.
Main Methods:
- Constructed a bilayer network to model relationships between miRNAs and diseases.
- Employed non-negative matrix factorization to predict disease miRNA candidates.
- Integrated miRNA functional similarity, disease similarity, and known miRNA-disease associations.
- Incorporated the sparseness characteristic of disease miRNAs for a more reliable model.
Main Results:
- The proposed method effectively integrates multiple data sources within a bilayer network.
- It accurately predicts disease miRNA candidates for both well-characterized and novel diseases.
- A case study demonstrated the method's capability in discovering potential disease miRNAs across various neoplasms.
Conclusions:
- The developed method offers a superior approach for disease miRNA prediction.
- It successfully addresses the challenge of integrating complex biological information.
- The findings contribute to a better understanding of disease etiology and pathogenesis through miRNA identification.
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