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Research on RNA modification in disease diagnosis and prognostic biomarkers: current status and challenges

  • 0School of Opto-electronic and Communication Engineering, Xiamen University of Technology, Ligong Road, Jimei District, Xiamen, Fujian 361024, China.
Briefings in Bioinformatics +

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July 23, 2025

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July 23, 2025

View abstract on PubMed

Summary

This summary is machine-generated.

RNA modifications are altered in diseases, offering potential as diagnostic biomarkers. This review explores machine learning methods for identifying these RNA modification biomarkers for disease diagnosis and prognosis.

Area Of Science

  • Molecular Biology
  • Genomics
  • Biochemistry

Background

  • RNA modifications are vital post-transcriptional regulators in cellular processes.
  • Aberrant RNA modification levels are linked to various human diseases.
  • Specific modifications like m6A, m1A, and A-to-I editing show disease-associated alterations.

Purpose Of The Study

  • To review statistical and machine learning methods for identifying RNA modification-related biomarkers.
  • To explore the role of RNA modifications in disease diagnosis and prognosis.
  • To provide insights into mining RNA modification biomarkers for clinical applications.

Main Methods

  • Review of statistical and machine learning techniques.
  • Analysis of bioinformatics tools and evaluation metrics.
  • Exploration of common RNA modifications, modifying proteins, and mechanisms.

Main Results

  • Significant alterations in RNA modifications are observed across multiple diseases.
  • RNA modifications and their regulatory factors show promise as disease biomarkers.
  • Machine learning algorithms are effective in discovering RNA modification-related biomarkers.

Conclusions

  • RNA modification biomarkers have potential for early disease diagnosis and prognosis.
  • Further research is needed to overcome challenges in clinical application.
  • This review serves as a reference for RNA modification biomarker discovery.

Keywords:

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