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MicroRNAs

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MicroRNA (miRNA) are short, regulatory RNA transcribed from introns (non-coding regions of a gene) or intergenic regions (stretches of DNA present between genes). Several processing steps are required to form biologically active, mature miRNA. The initial transcript, called primary miRNA (pri-mRNA), base-pairs with itself, forming a stem-loop structure. Within the nucleus, an endonuclease enzyme, called Drosha, shortens the stem-loop structure into hairpin-shaped pre-miRNA. After the pre-miRNA...
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WVMDA: Predicting miRNA-Disease Association Based on Weighted Voting.

Zhen-Wei Zhang1, Zhen Gao2, Chun-Hou Zheng1,2

  • 1School of Cyberspace Security, Qufu Normal University, Qufu, China.

Frontiers in Genetics
|October 18, 2021
PubMed
Summary

This study introduces a novel weighted voting model for predicting microRNA (miRNA)-disease associations. The model enhances accuracy by incorporating credibility similarity and a filtering mechanism to identify potential disease-related miRNAs.

Keywords:
credibility similaritydiseasemiRNAmiRNA-disease associationweighted voting

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Area of Science:

  • Biomedical Informatics
  • Genomics
  • Computational Biology

Background:

  • MicroRNA (miRNA) expression profiles are increasingly linked to human diseases.
  • miRNA expression may serve as a clinical diagnostic indicator for complex diseases.
  • Accurate prediction of miRNA-disease associations is crucial for disease prevention and treatment.

Purpose of the Study:

  • To develop a robust model for predicting miRNA-disease associations.
  • To improve the accuracy of miRNA-disease association prediction by addressing data incompleteness and noise.

Main Methods:

  • Proposed a weighted voting-based model for miRNA-disease association prediction (WVMDA).
  • Introduced credibility similarity, based on known association reliability, to enhance network construction.
  • Developed a filtering mechanism to reduce noise while preserving reliable similarity information.
  • Focused on designing a fair and efficient weighting strategy for the voting process.

Main Results:

  • WVMDA demonstrated efficacy in identifying miRNAs associated with diseases through cross-validation.
  • Case studies confirmed the model's ability to accurately predict miRNA-disease associations.
  • The model successfully integrated credibility similarity and filtering to improve prediction accuracy.

Conclusions:

  • The WVMDA model offers an efficient and reliable method for predicting miRNA-disease associations.
  • This approach can aid in the discovery of novel biomarkers for clinical diagnosis and therapeutic targets.
  • The study highlights the importance of robust similarity measures and noise reduction in network-based prediction models.