Prioritization of disease microRNAs through a human phenome-microRNAome network

Jiang, Qinghua; Hao, Yangyang; Wang, Guohua; Juan, Liran; Zhang, Tianjiao; Teng, Mingxiang; Liu, Yunlong; Wang, Yadong

doi:10.1186/1752-0509-4-s1-s2

Cited by 331 publications

(248 citation statements)

References 61 publications

(67 reference statements)

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“…Specific microRNAs (miRNAs) have been implicated in both lung development and disease (Jiang et al, 2010;Ornitz and Yin, 2012). In the epithelium, mice with loss of function of members of the miR-17 family show early lethality and hypoplastic lungs, whereas overexpression results in hyperproliferation and inhibition of differentiation of epithelial progenitors (Lu et al, 2007;Ventura et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

miR-142-3pbalances proliferation and differentiation of mesenchymal cells during lung development

et al. 2014

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The regulation of the balance between proliferation and differentiation in the mesenchymal compartment of the lung is largely uncharacterized, unlike its epithelial counterpart. In this study, we determined that miR-142-3p contributes to the proper proliferation of mesenchymal progenitors by controlling the level of WNT signaling. miR-142-3p can physically bind to adenomatous polyposis coli mRNA, functioning to regulate its expression level. In miR-142-3p loss-of-function experiments, proliferation of parabronchial smooth muscle cell progenitors is significantly impaired, leading to premature differentiation. Activation of WNT signaling in the mesenchyme, or Apc loss of function, can both rescue miR-142-3p knockdown. These findings show that in the embryonic lung mesenchyme, the microRNA machinery modulates the level of WNT signaling, adding an extra layer of control in the feedback loop between FGFR2C and β-cateninmediated WNT signaling.

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Section: Introductionmentioning

confidence: 99%

miR-142-3pbalances proliferation and differentiation of mesenchymal cells during lung development

et al. 2014

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“…Under the assumption that miRNAs with similar functions tend to be associated to phenotypically similar diseases[12,13]. Jiang et al proposed the first computational model based on hypergeometric distribution to predict new miRNA-disease associations[14], in which they integrated the phenotypic similarity network of diseases, the miRNA functional similarity network as well as the known human disease-miRNA association networks. Xu et al introduced a network-centric approach to prioritize candidate disease miRNAs by constructing four topological features that are distinguishable between prostate cancer (PC) and non-PC miRNAs[15].…”

Section: Introductionmentioning

confidence: 99%

GLNMDA: a novel method for miRNA-disease association prediction based on global linear neighborhoods

Liang

Xiao

et al. 2018

RNA Biology

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Recently, increasing studies have shown that miRNAs are involved in the development and progression of various complex diseases. Consequently, predicting potential miRNA-disease associations makes an important contribution to understanding the pathogenesis of diseases, developing new drugs as well as designing individualized diagnostic and therapeutic approaches for different human diseases. Nonetheless, the inherent noise and incompleteness in the existing biological datasets have limited the prediction accuracy of current computational models. To solve this issue, in this paper, we propose a novel method for miRNA-disease association prediction based on global linear neighborhoods (GLNMDA). Specifically, our method obtains a new miRNA/disease similarity matrix by linearly reconstructing each miRNA/disease according to the known experimentally verified miRNA-disease associations. We then adopt label propagation to infer the potential associations between miRNAs and diseases. As a result, GLNMDA achieved reliable performance in the frameworks of both local and global LOOCV (AUCs of 0.867 and 0.929, respectively) and 5-fold cross validation (average AUC of 0.926). Case studies on five common human diseases further confirmed the utility of our method in discovering latent miRNA-disease pairs. Taken together, GLNMDA could serve as a reliable computational tool for miRNA-disease association prediction.

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“…Therefore, it is highly desirable to develop complementary computational methods that can quickly predict potential disease-related miRNA candidates for experimental studies. Jiang et al (2010), showed that functionally related miRNAs tend to be associated with phenotypically similar diseases. They constructed an miRNA network by establishing a functional relationship between two miRNAs based on their predicted target genes.…”

Section: Introductionmentioning

confidence: 99%

Prediction of disease-related microRNAs by incorporating functional similarity and common association information

Han¹,

Xuan²,

Ding³

et al. 2014

Genet. Mol. Res.

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ABSTRACT. The identification of human disease-related microRNAs (miRNAs) is important for understanding the pathogenesis of diseases, but to do this experimentally is a costly and time-consuming process. Computational prediction of disease-related miRNA candidates is a valuable complement to experimental studies. It is essential to develop an effective prediction method to provide reliable candidates for subsequent biological experiments. In this study, we constructed a miRNA functional similarity network based on calculation of the functional similarity between each pair of miRNAs. Here, we present a new method (DismiPred) for predicting disease-related miRNA candidates based on the network. This method incorporates functional similarity and common association information to achieve an efficient prediction performance. DismiPred has been successfully shown to recover experimentally validated disease-related miRNAs for 12 common human diseases, with an F-measure ranging from 69.49 to 91.69%. Furthermore, a case study examining breast neoplasms showed that DismiPred could uncover novel disease-related miRNAs. DismiPred is useful for further experimental studies on the involvement of miRNAs in the pathogenesis of diseases.

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Prioritization of disease microRNAs through a human phenome-microRNAome network

Cited by 331 publications

References 61 publications

miR-142-3pbalances proliferation and differentiation of mesenchymal cells during lung development

miR-142-3pbalances proliferation and differentiation of mesenchymal cells during lung development

GLNMDA: a novel method for miRNA-disease association prediction based on global linear neighborhoods

Prediction of disease-related microRNAs by incorporating functional similarity and common association information

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