Prediction of microRNAs Associated with Human Diseases Based on Weighted k Most Similar Neighbors

Xuan, Ping; Han, Koeun; Guo, Maozu; Guo, Yahong; Li, Jinbao; Ding, Jian; Liu, Yong; Dai, Qigen; Jin, Li; Teng, Zhaogang; Huang, Yufei

doi:10.1371/journal.pone.0070204

Cited by 282 publications

(261 citation statements)

References 52 publications

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“…In addition, RWRMDA does not recognize the miRNA family or cluster information and is incapable of predicting novel miRNAs for diseases without any known related miRNAs (isolated diseases). Xuan et al 41. presented an algorithm (called HDMP) to predict miRNA-disease associations.…”

mentioning

confidence: 99%

Network Consistency Projection for Human miRNA-Disease Associations Inference

Liao

et al. 2016

Sci Rep

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Prediction and confirmation of the presence of disease-related miRNAs is beneficial to understand disease mechanisms at the miRNA level. However, the use of experimental verification to identify disease-related miRNAs is expensive and time-consuming. Effective computational approaches used to predict miRNA-disease associations are highly specific. In this study, we develop the Network Consistency Projection for miRNA-Disease Associations (NCPMDA) method to reveal the potential associations between miRNAs and diseases. NCPMDA is a non-parametric universal network-based method that can simultaneously predict miRNA-disease associations in all diseases but does not require negative samples. NCPMDA can also confirm the presence of miRNAs in isolated diseases (diseases without any known miRNA association). Leave-one-out cross validation and case studies have shown that the predictive performance of NCPMDA is superior over that of previous method.

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mentioning

confidence: 99%

Network Consistency Projection for Human miRNA-Disease Associations Inference

Liao

et al. 2016

Sci Rep

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“…1(a). It can be seen that, in the intra-family selected miRNA groups, the functional similarity score calculated by MFSP is higher than that of Wang’s method32 and Xuan’s method34, which implemented with the same version of database. Meanwhile, the functional similarity score calculated by MFSP is lower than that of other methods in terms of inter-family groups.…”

Section: Resultsmentioning

confidence: 93%

“…In this method, an inferring GO term similarity algorithm was applied to measure the semantic similarity of diseases structured as directed acyclic33, and then the miRNA functional similarity was inferred by best-match average (BMA) method. Moreover, Xuan et al 34. improved the calculation of information content (IC) of diseases based on the intuition that the more general the disease term is and the less semantic contribution it has, which ensure higher reliability of semantic similarity of disease.…”

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confidence: 99%

A path-based measurement for human miRNA functional similarities using miRNA-disease associations

Ding

Luo

Xiao

et al. 2016

Sci Rep

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Compared with the sequence and expression similarity, miRNA functional similarity is so important for biology researches and many applications such as miRNA clustering, miRNA function prediction, miRNA synergism identification and disease miRNA prioritization. However, the existing methods always utilized the predicted miRNA target which has high false positive and false negative to calculate the miRNA functional similarity. Meanwhile, it is difficult to achieve high reliability of miRNA functional similarity with miRNA-disease associations. Therefore, it is increasingly needed to improve the measurement of miRNA functional similarity. In this study, we develop a novel path-based calculation method of miRNA functional similarity based on miRNA-disease associations, called MFSP. Compared with other methods, our method obtains higher average functional similarity of intra-family and intra-cluster selected groups. Meanwhile, the lower average functional similarity of inter-family and inter-cluster miRNA pair is obtained. In addition, the smaller p-value is achieved, while applying Wilcoxon rank-sum test and Kruskal-Wallis test to different miRNA groups. The relationship between miRNA functional similarity and other information sources is exhibited. Furthermore, the constructed miRNA functional network based on MFSP is a scale-free and small-world network. Moreover, the higher AUC for miRNA-disease prediction indicates the ability of MFSP uncovering miRNA functional similarity.

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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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Prediction of microRNAs Associated with Human Diseases Based on Weighted k Most Similar Neighbors

Cited by 282 publications

References 52 publications

Network Consistency Projection for Human miRNA-Disease Associations Inference

Network Consistency Projection for Human miRNA-Disease Associations Inference

A path-based measurement for human miRNA functional similarities using miRNA-disease associations

NDAMDA: Network distance analysis for MiRNA‐disease association prediction

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