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Related Experiment Video

Updated: Dec 28, 2025

Identification of RNAs Engaged in Direct RNA-RNA Interaction with a Long Non-Coding RNA

Identification of RNAs Engaged in Direct RNA-RNA Interaction with a Long Non-Coding RNA

Published on: July 9, 2021

Probing lncRNA-Protein Interactions: Data Repositories, Models, and Algorithms.

Lihong Peng¹, Fuxing Liu¹, Jialiang Yang²

¹School of Computer Science, Hunan University of Technology, Zhuzhou, China.

Frontiers in Genetics

|February 22, 2020

Summary

Computational methods accelerate the discovery of long non-coding RNA-protein interactions (LPIs), crucial for biological understanding. SFPEL-LPI demonstrated superior performance in predicting these vital interactions.

Keywords:

computational method data repositories lncRNA–protein interaction machine learning-based method network-based method

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Last Updated: Dec 28, 2025

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Probing High-density Functional Protein Microarrays to Detect Protein-protein Interactions

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Published on: August 2, 2015

Area of Science:

Molecular Biology
Bioinformatics

Background:

Long non-coding RNA-protein interactions (LPIs) are critical for cellular functions.
Experimental identification of LPIs is laborious and expensive.
Computational approaches offer an efficient alternative for LPI candidate prediction.

Purpose of the Study:

To review and compare computational methods for LPI prediction.
To evaluate the performance of network-based and machine learning-based models.
To identify the most effective LPI prediction strategies.

Main Methods:

Exploration of LPI data repositories.
Categorization of prediction models into network-based and machine learning-based (matrix factorization, ensemble learning).
Performance evaluation using Leave-One-Out cross-validation (LOOCV) and fivefold cross-validation.

Main Results:

SFPEL-LPI achieved the highest Area Under the Curve (AUC) among the evaluated models.
Machine learning-based methods, particularly ensemble learning, show promise.
Comparative analysis highlights the strengths and weaknesses of different computational strategies.

Conclusions:

Computational methods significantly advance LPI identification.
SFPEL-LPI represents a high-performing model for LPI prediction.
Further research is needed to overcome existing limitations and enhance predictive accuracy.