MicroRNAs and complex diseases: from experimental results to computational models

Abstract

Plenty of microRNAs (miRNAs) were discovered at a rapid pace in plants, green algae, viruses and animals. As one of the most important components in the cell, miRNAs play a growing important role in various essential and important biological processes. For the recent few decades, amounts of experimental methods and computational models have been designed and implemented to identify novel miRNA-disease associations. In this review, the functions of miRNAs, miRNA-target interactions, miRNA-disease associations and some important publicly available miRNA-related databases were discussed in detail. Specially, considering the important fact that an increasing number of miRNA-disease associations have been experimentally confirmed, we selected five important miRNA-related human diseases and five crucial disease-related miRNAs and provided corresponding introductions. Identifying disease-related miRNAs has become an important goal of biomedical research, which will accelerate the understanding of disease pathogenesis at the molecular level and molecular tools design for disease diagnosis, treatment and prevention. Computational models have become an important means for novel miRNA-disease association identification, which could select the most promising miRNA-disease pairs for experimental validation and significantly reduce the time and cost of the biological experiments. Here, we reviewed 20 state-of-the-art computational models of predicting miRNA-disease associations from different perspectives. Finally, we summarized four important factors for the difficulties of predicting potential disease-related miRNAs, the framework of constructing powerful computational models to predict potential miRNA-disease associations including five feasible and important research schemas, and future directions for further development of computational models.

New Statistical Methods for Constructing Robust Differential Correlation Networks

“…Then we generated random numbers of Z1 and Z2 via the formula 1 = 1 1 + ( 1 ) 1 for cases and 2 = 2 2 + ( 2 ) 2 for controls. The choices for ( ), = 1, 2 were taken to be 1 ( ) = 1( 1), 2 ( ) = | | + 1( 2), 3 To evaluate the type I error rates of the tests, we considered two scenarios: (1) 1 = 2 = 0; and (2) 1 = 2 = 1; To evaluate the powers of the tests, we set 1 = 0 and 2 = 1. So we totally have 4 × 3 × 3 × 2 = 72 scenarios for evaluating Type I error rates and 4 × 3 × 3 × 1 = 36 scenarios for evaluating powers.…”

“…Each miRNA can interact with hundreds of genes and plays various roles in tumorigenesis, metastasis, proliferation. 3 In addition, a single gene can also be targeted by multiple miRNAs, which constitutes complex miRNA-target interactions. 3 The interplay among miRNAs plays an important role in the development of complex human diseases.…”

“…3 In addition, a single gene can also be targeted by multiple miRNAs, which constitutes complex miRNA-target interactions. 3 The interplay among miRNAs plays an important role in the development of complex human diseases. The network provides a natural way to model the interactions among miRNAs, with nodes representing miRNAs and edges representing interactions between miRNAs.…”

BRWSP: Predicting circRNA‐Disease Associations Based on Biased Random Walk to Search Paths on a Multiple Heterogeneous Network

“…Due to the low number of known circRNA-disease associations in the past years, machine learning methods are not widely used in the identification of circRNA-disease associations. However, the research progression of prediction in miRNAdisease association and lncRNA-disease association would benefit the development of computational models for circRNAs [28][29][30]. Recently, Fan et al [7] constructed CircR2Disease database by using the method of literature retrieval, which provides 661 circRNAs, 100 diseases, and 725 circRNA-disease associations.…”

In silico prediction of potential miRNA‐disease association using an integrative bioinformatics approach based on kernel fusion

“…There are many computational methods proposed to predict the potential associations between miRNAs and diseases, most of which are developed based on the assumption that miRNAs with similar functions are more likely to have connections with diseases of similar phenotypes. [16][17][18][19][20][21] Every time a new model was proposed, the prediction accuracy would be increased. In 2010, a hypergeometric distribution-based model was presented by Jiang et al 22 to predict miRNA-disease associations, where disease phenotype similarity, miRNA functional similarity and known human disease-miRNA associations were integrated.…”