MicroRNAs and complex diseases: from experimental results to computational models

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.