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Updated: Feb 20, 2026

CRISPR Gene Editing Tool for MicroRNA Cluster Network Analysis
Published on: April 25, 2022
MicroRNAs and complex diseases: from experimental results to computational models
1School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, China.
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.
Insights
MicroRNAs (miRNAs) are key cellular components involved in biological processes. This review details computational models for identifying miRNA-disease associations, aiding disease understanding and treatment.
Area of Science:
- Biochemistry
- Genetics
- Bioinformatics
Background:
- MicroRNAs (miRNAs) are crucial regulatory molecules in diverse biological processes across various organisms.
- Identifying miRNA-disease associations is vital for understanding disease pathogenesis and developing diagnostic/therapeutic tools.
- Numerous experimental and computational methods have been developed to discover novel miRNA-disease links.
Purpose of the Study:
- To review the functions of miRNAs, their target interactions, and their association with human diseases.
- To provide an overview of important miRNA-related databases.
- To critically analyze state-of-the-art computational models for predicting miRNA-disease associations.
Main Methods:
- Comprehensive literature review of miRNA functions, interactions, and disease associations.
- Selection and introduction of five key human diseases and five crucial disease-related miRNAs.
- Systematic review and categorization of 20 computational models for miRNA-disease association prediction.
Main Results:
- Detailed discussion on miRNA functions, target interactions, and established miRNA-disease links.
- Introduction to significant publicly available miRNA databases.
- Analysis of 20 computational models, highlighting their strengths and perspectives in predicting miRNA-disease associations.
Conclusions:
- Identifying disease-related miRNAs accelerates molecular-level understanding of pathogenesis and aids in developing diagnostic and therapeutic tools.
- Computational models are essential for efficiently predicting promising miRNA-disease pairs, reducing experimental costs and time.
- The review outlines challenges, a framework for powerful predictive models, and future research directions in miRNA-disease association prediction.

