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Updated: Jan 25, 2026

A Protocol for Computer-Based Protein Structure and Function Prediction

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

LPI-IBNRA: Long Non-coding RNA-Protein Interaction Prediction Based on Improved Bipartite Network Recommender

Guobo Xie¹, Cuiming Wu¹, Yuping Sun¹

¹School of Computers, Guangdong University of Technology, Guangzhou, China.

Frontiers in Genetics

|May 7, 2019

Summary

Predicting long non-coding RNA-protein interactions is crucial. A new bioinformatics method, LPI-IBNRA, uses an improved bipartite network recommender algorithm to accurately identify these interactions, outperforming existing approaches.

Keywords:

bipartite network interaction prediction lncRNA protein second-order correlation elimination

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Last Updated: Jan 25, 2026

A Protocol for Computer-Based Protein Structure and Function Prediction

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Published on: November 3, 2011

Optical Tweezers to Study RNA-Protein Interactions in Translation Regulation

Optical Tweezers to Study RNA-Protein Interactions in Translation Regulation

Published on: February 12, 2022

Identification of Coding and Non-coding RNA Classes Expressed in Swine Whole Blood

Identification of Coding and Non-coding RNA Classes Expressed in Swine Whole Blood

Published on: November 28, 2018

Area of Science:

Bioinformatics
Molecular Biology
Computational Biology

Background:

Long non-coding RNAs (lncRNAs) are vital regulators of biological processes through protein interactions.
Experimental identification of lncRNA-protein interactions is challenging, expensive, and time-consuming.
Computational methods are essential for predicting these interactions efficiently.

Purpose of the Study:

To develop a novel computational approach for predicting lncRNA-protein interactions.
To improve the accuracy and efficiency of predicting lncRNA-protein interactions.
To introduce the Long non-coding RNA-Protein Interaction Prediction based on Improved Bipartite Network Recommender Algorithm (LPI-IBNRA).

Main Methods:

Implementation of a two-round resource allocation strategy on a bipartite network.
Elimination of second-order correlations within the bipartite network.
Utilizing an improved bipartite network recommender algorithm for prediction.

Main Results:

The proposed LPI-IBNRA method demonstrated superior performance compared to five existing methods.
Achieved an AUC of 0.8932 in leave-one-out cross-validation (LOOCV).
Obtained an AUC of 0.8819 ± 0.0052 in 10-fold cross-validation, with case studies confirming predictive power.

Conclusions:

LPI-IBNRA offers a highly effective computational tool for predicting lncRNA-protein interactions.
The method provides a valuable alternative to experimental approaches for identifying these crucial molecular partnerships.
The improved bipartite network strategy enhances prediction accuracy in lncRNA-protein interaction studies.