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Quantifying the massive pleiotropy of microRNA: a human microRNA-disease causal association database generated with

K Rowan Wang1, Julian Hecker1,2, Michael J McGeachie2

  • 1Harvard University, Cambridge, MA, 02138, USA.

Biorxiv : the Preprint Server for Biology
|July 19, 2024
PubMed

Insights

MicroRNAs (miRNAs) regulate many diseases, but their multiple roles (pleiotropy) are unclear. This study quantifies miRNA pleiotropy using AI, revealing key characteristics and creating a valuable disease database for researchers.

Area of Science:

  • Genetics and Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • MicroRNAs (miRNAs) are crucial regulators in human diseases.
  • The phenomenon of miRNA pleiotropy, where one miRNA affects multiple diseases, is biologically significant but poorly understood.
  • Quantifying miRNA pleiotropy is essential for understanding their broad roles in health and disease.

Purpose of the Study:

  • To systematically quantify miRNA pleiotropy.
  • To identify biological and bioinformatic characteristics associated with increased miRNA pleiotropy.
  • To develop a comprehensive database of human miRNA-disease causal associations using artificial intelligence.

Main Methods:

  • Development of the miRNA Artificial Intelligence Disease Database (miRAIDD) utilizing large language models (LLM).
  • Systematic quantification of miRNA pleiotropy based on curated miRNA-disease associations.
  • Analysis of biological and bioinformatic features of miRNAs to correlate with pleiotropy.

Main Results:

  • Successful quantification of miRNA pleiotropy, highlighting its translational importance.
  • Identification of specific miRNA characteristics linked to higher pleiotropic activity.
  • Creation and release of the miRAIDD database, providing a valuable resource for the research community.

Conclusions:

  • The study provides a quantitative framework for understanding miRNA pleiotropy.
  • Specific miRNA features can predict their involvement in multiple diseases.
  • The miRAIDD database and associated AI methodologies offer significant advancements for miRNA research in human diseases.

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