RWRMDA: predicting novel human microRNA–disease associations

Chen, Xing; Liu, Ming-Xi; Ge, Yan

doi:10.1039/c2mb25180a

Cited by 388 publications

(332 citation statements)

References 53 publications

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“…We implemented local and global LOOCV to evaluate the prediction accuracy of NDAMDA and 6 previous computational models: WBSMDA,20 RLSMDA,24 MCMDA,28 HDMP,21 RWRMDA 19 and MiRAI 22. In LOOCV, each known association was used as the validation sample and the remaining known associations were regarded as the training samples.…”

Section: Resultsmentioning

confidence: 99%

NDAMDA: Network distance analysis for MiRNA‐disease association prediction

Chen

Wang

Huang

2018

J Cellular Molecular Medi

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In recent years, microRNAs (miRNAs) are attracting an increasing amount of researchers’ attention, as accumulating studies show that miRNAs play important roles in various basic biological processes and that dysregulation of miRNAs is connected with diverse human diseases, particularly cancers. However, the experimental methods to identify associations between miRNAs and diseases remain costly and laborious. In this study, we developed a computational method named Network Distance Analysis for MiRNA‐Disease Association prediction (NDAMDA) which could effectively predict potential miRNA‐disease associations. The highlight of this method was the use of not only the direct network distance between 2 miRNAs (diseases) but also their respective mean network distances to all other miRNAs (diseases) in the network. The model's reliable performance was certified by the AUC of 0.8920 in global leave‐one‐out cross‐validation (LOOCV), 0.8062 in local LOOCV and the average AUCs of 0.8935 ± 0.0009 in fivefold cross‐validation. Moreover, we applied NDAMDA to 3 different case studies to predict potential miRNAs related to breast neoplasms, lymphoma, oesophageal neoplasms, prostate neoplasms and hepatocellular carcinoma. Results showed that 86%, 72%, 86%, 86% and 84% of the top 50 predicted miRNAs were supported by experimental association evidence. Therefore, NDAMDA is a reliable method for predicting disease‐related miRNAs.

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Section: Resultsmentioning

confidence: 99%

NDAMDA: Network distance analysis for MiRNA‐disease association prediction

Chen

Wang

Huang

2018

J Cellular Molecular Medi

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“…We found that this study extracted a larger number of disease-related miRNAs for most diseases (Figure 4). Furthermore, we compared the results with other miRNA prioritization methods [4]. We extracted the top 50 candidate miRNAs for breast cancer, colonic cancer, and lung cancer ( Figure 5).…”

Section: Prioritization Of Candidate Mirna On Multiple Cancersmentioning

confidence: 99%

“…Chen has proposed a Random Walk with Restart for MiRNA-Disease prediction (RWRMDA). He proposed that the accuracy to determine the disease-related miRNAs increases by using the global network [4]. Chen utilized a semi-supervised classifier based method (miREFScan) to predict novel disease-related interactions between miRNAs and EFs [5].…”

Section: Introductionmentioning

confidence: 99%

A method of extracting disease-related microRNAs through the propagation algorithm using the environmental factor based global miRNA network

Kim

Yong-Jin

et al. 2015

BME

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Abstract. MicroRNAs (miRNA) are known to be involved in the development of various diseases. Hence various scientists in the field have been utilized computational analyses to determine the relationship between miRNA and diseases. However, the knowledge of miRNA and disease is still very limited. Therefore, we combined Environmental Factor (EF) data to a miRNA global network. Increasing research has shown that relationship between miRNAs and EFs play a significant role in classifying types of diseases. Environmental Factors consist of radiation, drugs, viruses, alcohol, cigarettes, and stress. Our global network considered all the interactions between every pair of miRNAs, which has led to precise analyses in comparison to local networks. As a result, our approaches' performance demonstrated its effectiveness in identifying diseaserelated miRNA and this is the area under the ROC curve (AUC) of 74.46%. Furthermore, comparative experiment has shown that our approach performs comparable to other existing methods with an accuracy of 94%, 90% and 96% for breast cancer, colonic cancer, and lung cancer respectively. In conclusion, these results support that our research has broadened new biological insights on identifying disease-related miRNAs.

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“…With the development of bioinformatics and the progress of miRNA-related projects, researches are gradually focused on the function of miRNAs. Existing studies have shown that miRNAs are involved in many important biological processes [15,16], like cell differentiation [17], proliferation [18], signal transduction [19], viral infection [20], and so on. Therefore, it is easy to find that miRNAs have close relationship with various human complex diseases [12,[21][22][23][24][25][26].…”

Section: Introductionmentioning

confidence: 99%

“…However, using experimental methods to identify miRNA-disease association is expensive and time-consuming. As the miRNA-related theories are becoming more and more common, such as the prediction model about miRNA and disease, the function of miRNA in biological processes, and signaling pathways, new therapies are urgently needed for the treatment of complex disease; it is necessary to develop powerful computational methods to reveal potential miRNA-disease associations [12,15,20,[34][35][36][37][38][39][40].…”

Section: Introductionmentioning

confidence: 99%

miRNA-Disease Association Prediction with Collaborative Matrix Factorization

et al. 2017

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As one of the factors in the noncoding RNA family, microRNAs (miRNAs) are involved in the development and progression of various complex diseases. Experimental identification of miRNA-disease association is expensive and time-consuming. Therefore, it is necessary to design efficient algorithms to identify novel miRNA-disease association. In this paper, we developed the computational method of Collaborative Matrix Factorization for miRNA-Disease Association prediction (CMFMDA) to identify potential miRNA-disease associations by integrating miRNA functional similarity, disease semantic similarity, and experimentally verified miRNA-disease associations. Experiments verified that CMFMDA achieves intended purpose and application values with its short consuming-time and high prediction accuracy. In addition, we used CMFMDA on Esophageal Neoplasms and Kidney Neoplasms to reveal their potential related miRNAs. As a result, 84% and 82% of top 50 predicted miRNA-disease pairs for these two diseases were confirmed by experiment. Not only this, but also CMFMDA could be applied to new diseases and new miRNAs without any known associations, which overcome the defects of many previous computational methods.

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RWRMDA: predicting novel human microRNA–disease associations

Cited by 388 publications

References 53 publications

NDAMDA: Network distance analysis for MiRNA‐disease association prediction

NDAMDA: Network distance analysis for MiRNA‐disease association prediction

A method of extracting disease-related microRNAs through the propagation algorithm using the environmental factor based global miRNA network

miRNA-Disease Association Prediction with Collaborative Matrix Factorization

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