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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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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...
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A graph auto-encoder model for miRNA-disease associations prediction.

Zhengwei Li1, Jiashu Li2, Ru Nie2

  • 1Engineering Research Center of Mine Digitalization of Ministry of Education and School of Computer Science and Technology, China University of Mining and Technology.

Briefings in Bioinformatics
|July 23, 2021
PubMed
Summary

This study introduces GAEMDA, a novel computational model using graph neural networks to predict microRNA-disease associations. GAEMDA efficiently identifies potential biomarkers, aiding disease research and clinical applications.

Keywords:
complex diseasegraph auto-encodergraph neural networksheterogeneous graphmiRNAmiRNA-disease associations prediction

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • MicroRNAs (miRNAs) play crucial roles in human complex diseases.
  • Identifying disease-related miRNAs is vital for biomarker discovery and understanding disease pathology.
  • Experimental validation of miRNA-disease associations is costly and time-consuming.

Purpose of the Study:

  • To develop an effective computational method for predicting potential miRNA-disease associations.
  • To address the need for efficient and cost-effective identification of disease-related miRNAs.
  • To leverage graph neural networks for accurate miRNA-disease association prediction.

Main Methods:

  • Proposed a novel graph auto-encoder model named GAEMDA.
  • Utilized a graph neural network-based encoder with aggregator functions and multi-layer perceptrons for node information aggregation.
  • Employed a bilinear decoder for predicting potential links between miRNA and disease nodes.

Main Results:

  • GAEMDA achieved an average area under the curve of 93.56±0.44% via 5-fold cross-validation.
  • Case studies on colon, esophageal, and kidney neoplasms showed high accuracy.
  • 48 out of 50 top predicted miRNAs were validated against established databases.

Conclusions:

  • The GAEMDA model demonstrates high predictive performance for miRNA-disease associations.
  • GAEMDA can serve as a reliable tool to guide future research on miRNA regulatory roles.
  • The computational approach offers a cost-effective alternative to experimental validation for biomarker discovery.