Inferring the human microRNA functional similarity and functional network based on microRNA-associated diseases

Wang, Dong; Wang, Juan; Lu, Ming; Song, Fei; Cui, Qinghua

doi:10.1093/bioinformatics/btq241

Cited by 784 publications

(685 citation statements)

References 24 publications

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“…The miRNA functional similarity used in this paper was calculated by Wang et al and can be downloaded directly at (http://www.cuilab.cn/files/images/cuilab/misim.zip) [62]. We used M to denote the miRNA functional similarity network, where each element M ij represents the functional similarity score between miRNA m(i) and m(j) .…”

Section: Methodsmentioning

confidence: 99%

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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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Section: Methodsmentioning

confidence: 99%

“…Mesh database (http://www.ncbi.nlm.nih.gov/) is a strict system for disease classification and is a credible dataset for effectively researching the relationship between different diseases[62]. The relationship between different diseases can be described through a structure of Directed Acyclic Graph ( DAG ).…”

Section: Methodsmentioning

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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“…The clustered miRNAs are usually transcribed together and are likely associated with similar diseases (Baskerville and Bartel, 2005;Wang et al, 2010). Five miRNAs (hsa-mir-16, hsa-mir-99a, hsa-mir-106a, hsa-mir-192, and hsa-mir-27b) are clustered with other miRNAs that have been experimentally validated to be associated with breast neoplasms.…”

Section: Hsa-mir-193amentioning

confidence: 99%

“…Since the seed of an miRNA is commonly required to be perfectly complementary to the target mRNAs for cleavage or translational repression, miRNAs of the same family likely regulate a common set of mRNA targets. Therefore, it is more likely that they are associated with similar diseases (Yu et al, 2007;Wang et al, 2010). The 6 miRNAs (hsa-mir-16, hsa-let-7b, hsa-mir-99a, hsa-mir-192, hsa-mir-27b, and hsa-mir-193a) and other breast neoplasm-related miRNAs belong to the respective miRNA families.…”

Section: Hsa-mir-193amentioning

confidence: 99%

“…We developed a new prediction method called DismiPred that is based on the central hypothesis offered in several previous studies that miRNAs with similar functions are often involved in similar diseases, and vice versa (Goh et al, 2007;Lu et al, 2008;Bandyopadhyay et al, 2010;Wang et al, 2010). The functional similarity between two miRNAs can be successfully measured by the semantic similarity of their associated diseases .…”

Section: Introductionmentioning

confidence: 99%

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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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Profiling and bioinformatic analysis reveal differential microRNA expression in the left and right kidneys in normal mice

et al. 2019

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Physiological and pathophysiological differences exist between the left and right kidneys; however, the molecular bases for these differences remain unknown. Since miRNAs are involved in kidney function and the development of kidney diseases, we examined their differential expression through miRNA profiling of the left and right kidneys of normal mice. We find that 36 miRNAs exhibit higher expression, whereas 22 miRNAs show lower expression in the left than the right kidneys in mice under physiological condition. Ten miRNAs were further examined by quantitative PCR assays, and four of them with high expression level were confirmed by Northern blot. Through bioinformatic analysis, we dissected the function and network of the differentially expressed miRNAs, providing insights into the physiological and pathophysiological differences between the left and the right kidneys.

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Inferring the human microRNA functional similarity and functional network based on microRNA-associated diseases

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 784 publications

References 24 publications

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

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

Profiling and bioinformatic analysis reveal differential microRNA expression in the left and right kidneys in normal mice

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