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Related Concept Videos

lncRNA - Long Non-coding RNAs02:39

lncRNA - Long Non-coding RNAs

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In humans, more than 80% of the genome gets transcribed. However, only around 2% of the genome codes for proteins. The remaining part produces non-coding RNAs which includes ribosomal RNAs, transfer RNAs, telomerase RNAs, and regulatory RNAs, among other types. A large number of regulatory non-coding RNAs have been classified into two groups depending upon their length – small non-coding RNAs, such as microRNA, which are less than 200 nucleotides in length, and long non-coding RNA...
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Updated: May 27, 2025

Identification of RNAs Engaged in Direct RNA-RNA Interaction with a Long Non-Coding RNA
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A heterogeneous information network learning model with neighborhood-level structural representation for predicting

Bo-Wei Zhao1, Xiao-Rui Su2, Yue Yang2

  • 1College of Computer and Information Science, School of Software, Southwest University, Chongqing 400715, China.

Computational and Structural Biotechnology Journal
|February 18, 2025
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Summary

This study introduces HINLMI, a computational model that integrates multiple biomolecular interactions to accurately predict long non-coding RNA-microRNA interactions (LMIs) for disease research.

Keywords:
Biological and network representationsHeterogeneous information networksLMIsNetwork structural representation

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Area of Science:

  • Biotechnology and Bioinformatics
  • Genomics and Molecular Biology

Background:

  • Long non-coding RNAs (lncRNAs) and microRNAs (miRNAs) are crucial in human disease mechanisms.
  • Experimental identification of lncRNA-miRNA interactions (LMIs) is laborious and time-consuming.
  • Existing computational methods often overlook complex biomolecular mechanisms, limiting prediction accuracy.

Purpose of the Study:

  • To develop an advanced computational model for precise prediction of lncRNA-miRNA interactions (LMIs).
  • To leverage heterogeneous information networks (HINs) for improved LMI prediction accuracy.
  • To integrate biological knowledge and network topology for a comprehensive understanding of LMIs.

Main Methods:

  • Construction of a heterogeneous information network (HIN) integrating nine types of biomolecular interactions.
  • Application of representation learning strategies to derive biological and network embeddings for lncRNAs and miRNAs.
  • Utilization of the XGBoost classifier with learned embeddings for predicting unknown LMIs.

Main Results:

  • HINLMI demonstrated superior performance compared to state-of-the-art models on real-world datasets.
  • The model accurately predicted LMIs by simultaneously considering biological knowledge and network topology.
  • Analysis confirmed the effectiveness of integrating rich heterogeneous information for identifying novel LMIs.

Conclusions:

  • HINLMI offers a powerful and accurate computational approach for predicting lncRNA-miRNA interactions.
  • Integrating diverse biomolecular data within a HIN framework enhances LMI prediction.
  • This method provides valuable insights for discovering novel lncRNA-miRNA interactions relevant to human diseases.