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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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An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
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The pentose sugar in DNA is deoxyribose, while in RNA the pentose sugar is ribose. The difference between the sugars is the presence of the hydroxyl group on the ribose's second carbon and a hydrogen on the deoxyribose's second carbon. The phosphate residue attaches to the hydroxyl group of the 5′ carbon of one sugar and the hydroxyl group of the 3′ carbon of the sugar of the next nucleotide, which forms  a 5′ to 3′ phosphodiester linkage.
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Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a...
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Related Experiment Video

Updated: Sep 13, 2025

Biotin-based Pulldown Assay to Validate mRNA Targets of Cellular miRNAs
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Incorporating graph representation and mutual attention mechanism for MiRNA-MRNA interaction prediction.

Tai-Long Shi1, Lei Wang2,3, Leon Wong4

  • 1School of Electronic Information, Xijing University, Xi'an, China.

Frontiers in Genetics
|August 1, 2025
PubMed
Summary

GRMMI accurately predicts microRNA-messenger RNA interactions by integrating sequence and graph data. This advancement aids in understanding gene regulation and discovering therapeutic targets for diseases.

Keywords:
BiLSTMGraRepfastTextmiRNA-Target mRNA interactionsmutual attention mechanisms

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Predicting microRNA-messenger RNA interactions is vital for understanding gene regulation and disease mechanisms.
  • Current prediction methods struggle with RNA sequence complexity and graph structural information.

Purpose of the Study:

  • To develop a novel framework, GRMMI, for enhanced prediction of microRNA-messenger RNA interactions.
  • To effectively integrate both sequence and graph-based features for improved prediction accuracy.

Main Methods:

  • GRMMI combines FastText-pretrained sequence embeddings with GraRep graph embeddings.
  • Employs antisense-aware sequence processing and a cross-sequence mutual attention architecture.
  • Integrates CNN-BiLSTM architecture with mutual attention for multi-dimensional information fusion.

Main Results:

  • GRMMI achieved an AUC of 0.9347 and an accuracy of 86.65% on the MTIS-9214 dataset.
  • Demonstrated superior performance compared to existing sequence-based methods.

Conclusions:

  • GRMMI effectively leverages multi-dimensional information for accurate RNA interaction prediction.
  • The framework shows practical utility in identifying biologically significant interactions for disease research and drug discovery.