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Updated: Nov 6, 2025

In Silico Identification and Characterization of circRNAs During Host-Pathogen Interactions

In Silico Identification and Characterization of circRNAs During Host-Pathogen Interactions

Published on: October 21, 2022

NSL2CD: identifying potential circRNA-disease associations based on network embedding and subspace learning.

Qiu Xiao¹, Yu Fu², Yide Yang³

¹Hunan Normal University and Hunan Xiangjiang Artificial Intelligence Academy, China.

Briefings in Bioinformatics

|May 6, 2021

Summary

This study introduces NSL2CD, a new computational method to predict circular RNA (circRNA)-disease associations. It efficiently identifies potential circRNA biomarkers for human diseases, aiding research and diagnosis.

Keywords:

circRNA–disease associations circular RNAs (circRNAs)disease-associated circRNAs network embedding subspace learning

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Last Updated: Nov 6, 2025

In Silico Identification and Characterization of circRNAs During Host-Pathogen Interactions

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Published on: October 21, 2022

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Published on: November 14, 2019

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Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Circular RNAs (circRNAs) are key regulators in human diseases and potential diagnostic biomarkers.
Discovering circRNA-disease relationships is crucial but challenging due to time and cost constraints of experimental methods.
Computational approaches are needed to identify candidate circRNAs for disease research and understanding pathogenesis.

Purpose of the Study:

To develop a novel computational method for predicting circRNA-disease associations.
To identify potential circRNA candidates linked to human diseases.
To enhance the understanding of circRNA functions and disease mechanisms.

Main Methods:

A network embedding-based adaptive subspace learning method (NSL2CD) was proposed.
Calculated circRNA and disease similarities using diverse data sources.
Employed network embedding for low-dimensional node representations.
Utilized an adaptive subspace learning model with graph regularization and L1,2-norm constraints for association prediction.

Main Results:

NSL2CD demonstrated comparable performance across various evaluation metrics.
The method effectively predicted potential circRNA-disease associations.
Case studies validated the model's capability in discovering disease-related circRNA candidates.

Conclusions:

NSL2CD offers an efficient computational approach for predicting circRNA-disease associations.
The method aids in identifying novel circRNA biomarkers for diseases.
This work contributes to understanding circRNA functions and disease pathogenesis.