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Long Noncoding RNA and Protein Interactions: From Experimental Results to Computational Models Based on Network

Hui Zhang1, Yanchun Liang2,3, Siyu Han4

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Long non-coding RNAs (lncRNAs) play crucial roles in gene regulation and human diseases. This study reviews network-based computational methods for predicting lncRNA-protein interactions, offering a valuable resource for researchers.

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biological network sciencecomputational modellncRNA–protein interaction predictionmachine learning

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Area of Science:

  • Genomics
  • Bioinformatics
  • Molecular Biology

Background:

  • Long non-coding RNAs (lncRNAs) are critical regulators of gene expression with implications in various human disorders.
  • lncRNA-protein interactions are vital for understanding lncRNA functions and annotations, but experimental methods are costly and time-consuming.
  • Computational approaches offer efficient alternatives for predicting these interactions.

Purpose of the Study:

  • To review and discuss state-of-the-art network-based computational methods for predicting long non-coding RNA-protein interactions.
  • To aid researchers in selecting appropriate methods for reliable lncRNA-protein interaction predictions.
  • To provide a curated collection of network data for user convenience.

Main Methods:

  • The study categorizes computational prediction methods into sequence-based and network-based approaches.
  • Focus is placed on network-based methods that leverage heterogeneous biological network data.
  • Key aspects discussed include data materials, interaction scoring algorithms, and method-specific advantages and disadvantages.

Main Results:

  • Network-based methods effectively capture topological features in biological networks, often missed by sequence-based approaches.
  • A comprehensive summary of current network-based algorithms for lncRNA-protein interaction prediction is presented.
  • Associated network data is collected and made available to facilitate research.

Conclusions:

  • Network-based computational methods are powerful tools for predicting lncRNA-protein interactions.
  • This review provides valuable insights for selecting optimal prediction strategies.
  • The shared data resource aims to accelerate research in lncRNA functional genomics.