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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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The Bipartite Network Projection-Recommended Algorithm for Predicting Long Non-coding RNA-Protein Interactions.

Qi Zhao1, Haifan Yu1, Zhong Ming2

  • 1School of Mathematics, Liaoning University, Shenyang 110036, China.

Molecular Therapy. Nucleic Acids
|November 3, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces LPI-BNPRA, a computational method to predict long non-coding RNA (lncRNA)-protein interactions. This approach offers a time-saving and cost-effective alternative to experimental methods for uncovering these crucial biological relationships.

Keywords:
lncRNAlncRNA-protein interaction predictionproteinrecommended algorithmsemi-supervised method

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Area of Science:

  • Biotechnology
  • Genomics
  • Bioinformatics

Background:

  • Non-coding RNAs (ncRNAs) are vital in biological processes like cell differentiation and disease.
  • Long non-coding RNAs (lncRNAs) constitute the majority of ncRNAs and exert functions via RNA-binding proteins.
  • Experimental validation of lncRNA-protein interactions is costly and time-consuming.

Purpose of the Study:

  • To develop a novel computational method for predicting lncRNA-protein interactions.
  • To provide a time-saving and cost-effective approach for identifying potential lncRNA-protein relationships.

Main Methods:

  • Proposed a semi-supervised method named LPI-BNPRA (lncRNA-protein interaction-bipartite network projection recommended algorithm).
  • Utilized lncRNA similarity matrix, protein similarity matrix, and lncRNA-protein interaction matrix.
  • Employed leave-one-out cross-validation for performance evaluation.

Main Results:

  • LPI-BNPRA achieved high-confidence results with an AUC of 0.8754 and AUPR of 0.6283.
  • Outperformed three previous computational methods in prediction accuracy.
  • Case studies on the Mus musculus dataset confirmed the reliability of the approach.

Conclusions:

  • LPI-BNPRA is a reliable computational tool for predicting lncRNA-protein interactions.
  • The method can significantly aid biomedical research by efficiently identifying these interactions.
  • Facilitates a deeper understanding of lncRNA functions in biological processes and diseases.