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Related Concept Videos

MicroRNAs01:22

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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Related Experiment Video

Updated: Jun 25, 2025

In Silico Identification and Characterization of circRNAs During Host-Pathogen Interactions
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BJLD-CMI: a predictive circRNA-miRNA interactions model combining multi-angle feature information.

Yi-Xin Zhao1, Chang-Qing Yu1, Li-Ping Li1,2

  • 1School of information Engineering, Xijing University, Xi'an, China.

Frontiers in Genetics
|May 27, 2024
PubMed
Summary

A new computational model, BJLD-CMI, accurately predicts circular RNA (circRNA) and microRNA (miRNA) interactions. This method accelerates disease research by identifying potential circRNA-miRNA interactions efficiently.

Keywords:
autoencodercircRNAcircRNA-miRNA interactiongraph embeddingmiRNA

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Area of Science:

  • Bioinformatics
  • Genomics
  • Computational Biology

Background:

  • Circular RNAs (circRNAs) play a role in human disease pathogenesis through microRNA (miRNA) binding.
  • Identifying circRNA-miRNA interactions is vital for disease diagnosis and treatment.
  • Traditional wet lab experiments for identifying these interactions are time-consuming and limited in scale.

Purpose of the Study:

  • To develop an efficient computational model for predicting circRNA-miRNA interactions.
  • To overcome the limitations of traditional experimental methods.

Main Methods:

  • The BJLD-CMI model extracts sequence features from circRNAs and miRNAs using Jaccard and Bert's methods.
  • It integrates these features to obtain circRNA-miRNA (CMI) attribute features.
  • Graph embedding (Line) extracts CMI behavioral features, and Autoencoder networks fuse attribute and behavioral features for prediction.

Main Results:

  • BJLD-CMI achieved high ROC curve areas (94.95% on CMI-9589, 90.69% on CMI-9905).
  • The model demonstrated superior performance compared to existing methods.
  • A case study confirmed 7 out of the top 10 predicted interactions through PubMed searches.

Conclusions:

  • BJLD-CMI is an effective computational tool for predicting circRNA-miRNA interactions.
  • The model can significantly aid biological wet experiments and reduce research burden.
  • It offers a valuable approach for advancing research in complex human diseases.