MISIM v2.0: a web server for inferring microRNA functional similarity based on microRNA-disease associations

Li, Jianwei; Zhang, Shan; Wan, Yanping; Zhao, Yingshu; Shi, Jiangcheng; Zhou, Yuan; Cui, Qinghua

doi:10.1093/nar/gkz328

Cited by 55 publications

(32 citation statements)

References 27 publications

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“…We conduct functional analysis for top-ranked miRNAs by integrating the co-expression similarity, co-GO similarity, coliterature similarity, and co-similar disease similarity by using miRNA functional analysis tool MISIM (Li et al, 2019). There are 14, 15, and 13 miRNAs respectively annotated by MISIM in the top 20 important miRNAs of the three cancer datasets.…”

Section: Most Of the Critical Mirnas Involve In Some Important Biologmentioning

confidence: 99%

A Systematic Way to Infer the Regulation Relations of miRNAs on Target Genes and Critical miRNAs in Cancers

et al. 2020

Front. Genet.

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MicroRNAs (miRNAs) are a class of important non-coding RNAs, which play important roles in tumorigenesis and development by targeting oncogenes or tumor suppressor genes. One miRNA can regulate multiple genes, and one gene can be regulated by multiple miRNAs. To promote the clinical application of miRNAs, two fundamental questions should be answered: what's the regulatory mechanism of a miRNA to a gene, and which miRNAs are important for a specific type of cancer. In this study, we propose a miRNA influence capturing (miRNAInf) to decipher regulation relations of miRNAs on target genes and identify critical miRNAs in cancers in a systematic approach. With the pair-wise miRNA/gene expression profiles data, we consider the assigning problem of a miRNA on target genes and determine the regulatory mechanisms by computing the Pearson correlation coefficient between the expression changes of a miRNA and that of its target gene. Furthermore, we compute the relative local influence strength of a miRNA on its target gene. Finally, integrate the local influence strength and target gene's importance to determine the critical miRNAs involved in specific cancer. Results on breast, liver and prostate cancers show that positive regulations are as common as negative regulations. The top-ranked miRNAs show great potential as therapeutic targets driving cancer to a normal state, and they are demonstrated to be closely related to cancers based on biological functional analysis, drug sensitivity/resistance analysis and survival analysis. This study will be helpful for the discovery of critical miRNAs and development of miRNAs-based clinical therapeutics.

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Section: Most Of the Critical Mirnas Involve In Some Important Biologmentioning

confidence: 99%

A Systematic Way to Infer the Regulation Relations of miRNAs on Target Genes and Critical miRNAs in Cancers

et al. 2020

Front. Genet.

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“…miRNA functional similarity scores were calculated based on the hypothesis that functionally similar miRNAs are more inclined to associate with phenotypically similar diseases. miRNA Functional similarity data misim 2.0 was downloaded from http://www.lirmed.com/misim/ to construct the N m × N m miRNA functional similarity matrix FS [33]. The similarity score between miRNA m(u) and m(i) can be expressed as FS(u,i).…”

Section: Mirna Expression Datamentioning

confidence: 99%

Improved Prediction of miRNA-Disease Associations Based on Matrix Completion with Network Regularization

Park

et al. 2020

Cells

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The identification of potential microRNA (miRNA)-disease associations enables the elucidation of the pathogenesis of complex human diseases owing to the crucial role of miRNAs in various biologic processes and it yields insights into novel prognostic markers. In the consideration of the time and costs involved in wet experiments, computational models for finding novel miRNA-disease associations would be a great alternative. However, computational models, to date, are biased towards known miRNA-disease associations; this is not suitable for rare miRNAs (i.e., miRNAs with a few known disease associations) and uncommon diseases (i.e., diseases with a few known miRNA associations). This leads to poor prediction accuracies. The most straightforward way of improving the performance is by increasing the number of known miRNA-disease associations. However, due to lack of information, increasing attention has been paid to developing computational models that can handle insufficient data via a technical approach. In this paper, we present a general framework—improved prediction of miRNA-disease associations (IMDN)—based on matrix completion with network regularization to discover potential disease-related miRNAs. The success of adopting matrix factorization is demonstrated by its excellent performance in recommender systems. This approach considers a miRNA network as additional implicit feedback and makes predictions for disease associations relevant to a given miRNA based on its direct neighbors. Our experimental results demonstrate that IMDN achieved excellent performance with reliable area under the receiver operating characteristic (ROC) area under the curve (AUC) values of 0.9162 and 0.8965 in the frameworks of global and local leave-one-out cross-validations (LOOCV), respectively. Further, case studies demonstrated that our method can not only validate true miRNA-disease associations but also suggest novel disease-related miRNA candidates.

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“…The collected data contains 9664 experimentally validated lncRNA-miRNA interactions which included 923 lncRNAs and 263 miRNAs. And we also collected human miRNA functional similarity data sets from MISIM v2.0 (Wang et al, 2010;Li et al, 2019) 2 which is a bioinformatics tool not only for calculating human miRNA functional similarity, but also for predicting potential functions of miRNAs. Finally, 263 human miRNA functional similarities were obtained.…”

Section: Mirna-based Lncrna Similarity Networkmentioning

confidence: 99%

Inferring lncRNA Functional Similarity Based on Integrating Heterogeneous Network Data

Zhao

Zhou

et al. 2020

Front. Bioeng. Biotechnol.

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Although lncRNAs lack the potential to be translated into proteins directly, their complicated and diversiform functions make them as a window into decoding the mechanisms of human physiological activities. Accumulating experiment studies have identified associations between lncRNA dysfunction and many important complex diseases. However, known experimentally confirmed lncRNA functions are still very limited. It is urgent to build effective computational models for rapid predicting of unknown lncRNA functions on a large scale. To this end, valid similarity measure between known and unknown lncRNAs plays a vital role. In this paper, an original model was developed to calculate functional similarities between lncRNAs by integrating heterogeneous network data. In this model, a novel integrated network was constructed based on the data of four single lncRNA functional similarity networks (miRNA-based similarity network, disease-based similarity network, GTEx expression-based network and NONCODE expression-based network). Using the lncRNA pairs that share the target mRNAs as the benchmark, the results show that this integrated network is more effective than any single networks with an AUC of 0.736 in the cross validation, while the AUC of four single networks were 0.703, 0.733, 0.611, and 0.602. To implement our model, a web server named IHNLncSim was constructed for inferring lncRNA functional similarity based on integrating heterogeneous network data. Moreover, the modules of network visualization and disease-based lncRNA function enrichment analysis were added into IHNLncSim. It is anticipated that IHNLncSim could be an effective bioinformatics tool for the researches of lncRNA regulation function studies.

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MISIM v2.0: a web server for inferring microRNA functional similarity based on microRNA-disease associations

Cited by 55 publications

References 27 publications

A Systematic Way to Infer the Regulation Relations of miRNAs on Target Genes and Critical miRNAs in Cancers

A Systematic Way to Infer the Regulation Relations of miRNAs on Target Genes and Critical miRNAs in Cancers

Improved Prediction of miRNA-Disease Associations Based on Matrix Completion with Network Regularization

Inferring lncRNA Functional Similarity Based on Integrating Heterogeneous Network Data

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