Dataset Information

Identifying Potential miRNAs-Disease Associations With Probability Matrix Factorization.

ABSTRACT: In recent years, miRNAs have been verified to play an irreplaceable role in biological processes associated with human disease. Discovering potential disease-related miRNAs helps explain the underlying pathogenesis of the disease at the molecular level. Given the high cost and labor intensity of biological experiments, computational predictions will be an indispensable alternative. Therefore, we design a new model called probability matrix factorization (PMFMDA). Specifically, we first integrate miRNA and disease similarity. Next, the known association matrix and integrated similarity matrix are utilized to construct a probability matrix factorization algorithm to identify potentially relevant miRNAs for disease. We find that PMFMDA achieves reliable performance in the frameworks of global leave-one-out cross validation (LOOCV) and 5-fold cross validation (AUCs are 0.9237 and 0.9187, respectively) in the HMDD (V2.0) dataset, significantly outperforming a few state-of-the-art methods including CMFMDA, IMCMDA, NCPMDA, RLSMDA, and RWRMDA. In addition, case studies show that PMFMDA has good predictive performance for new associations, and the evidence can be identified by literature mining.

SUBMITTER: Xu J

PROVIDER: S-EPMC6918542 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Identifying Potential miRNAs-Disease Associations With Probability Matrix Factorization.

Xu Junlin J Cai Lijun L Liao Bo B Zhu Wen W Wang Peng P Meng Yajie Y Lang Jidong J Tian Geng G Yang Jialiang J

Frontiers in genetics 20191211

In recent years, miRNAs have been verified to play an irreplaceable role in biological processes associated with human disease. Discovering potential disease-related miRNAs helps explain the underlying pathogenesis of the disease at the molecular level. Given the high cost and labor intensity of biological experiments, computational predictions will be an indispensable alternative. Therefore, we design a new model called probability matrix factorization (PMFMDA). Specifically, we first integrate ...[more]

PMID: 31921290

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Project description:With the rapid development of biological research, microRNAs (miRNA) have become an attractive topic because lots of experimental studies have revealed the significant associations between miRNAs and diseases. However, considering that experiments are expensive and time-consuming, computational methods for predicting associations between miRNAs and diseases have become increasingly crucial. In this study, we proposed a neighborhood regularized logistic matrix factorization method for miRNA-disease association prediction (NRLMFMDA) by integrating miRNA functional similarity, disease semantic similarity, Gaussian interaction profile kernel similarity, and experimentally validation of disease-miRNA association. We used Gaussian interaction profile kernel similarity to cover the shortage of the traditional similarity to make it more reasonable and complete. Furthermore, NRLMFMDA also considered the important influences of the neighborhood information and took full advantage of them to improve the accuracy of the miRNA-disease association prediction. We also improved the accuracy by giving higher weights to the known association data in the process of calculating the potential association probabilities. In the global and the local leave-one-out cross validation, NRLMFMDA got the AUCs of 0.9068 and 0.8239, respectively. Moreover, the average AUC of NRLMFMDA in 5-fold cross validation was 0.8976 ± 0.0034. All the three kinds of cross validations have shown significant advantages to a number of previous models. In the case studies of breast neoplasms, esophageal neoplasms and lymphoma according to known miRNA-disease associations in the recent version of HMDD database, there were 78, 80, and 74% of top 50 predicted related miRNAs verified to have associations with these three diseases, respectively. In the further case studies for new disease without any known related miRNAs and the previous version of HMDD database, there were also high proportions of the predicted miRNAs verified by experimental reports. All the validation experiment results have demonstrated the effectiveness and practicability of NRLFMDA to predict the potential miRNA-disease associations.

Project description:MotivationIdentification 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.ResultsWe 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 implementationThe web service for the new method and the list of predicted candidates for all the diseases are available at http://www.bioinfolab.top.Supplementary informationSupplementary data are available at Bioinformatics online.

Dataset Information

Identifying Potential miRNAs-Disease Associations With Probability Matrix Factorization.

Publications

Identifying Potential miRNAs-Disease Associations With Probability Matrix Factorization.

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