MicroRNAs and complex diseases: from experimental results to computational models

Xing Chen1, Di Xie2, Qi Zhao2

  • 1School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, China.

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.