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In Silico Identification and Characterization of circRNAs During Host-Pathogen Interactions
Published on: October 21, 2022
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NCPCDA: network consistency projection for circRNA-disease association prediction.
Guanghui Li1, Yingjie Yue2, Cheng Liang3
1School of Information Engineering, East China Jiaotong University Nanchang 330013 China ghli16@hnu.edu.cn.
RSC Advances
|May 9, 2022
Summary
This study introduces NCPCDA, a computational method for identifying circular RNA-disease associations. NCPCDA accurately predicts novel interactions, aiding disease pathology understanding and biomarker discovery.
Area of Science:
- Bioinformatics
- Genomics
- Computational Biology
Background:
- Circular RNAs (circRNAs) are crucial in biological processes and disease development.
- circRNAs show potential as disease biomarkers due to their stability and universality.
- Identifying circRNA-disease relationships is vital for understanding disease pathology.
Purpose of the Study:
- To develop a computational method for predicting novel circRNA-disease associations.
- To overcome the limitations of costly and laborious wet-lab experiments for discovering these interactions.
Main Methods:
- Developed NCPCDA, a network consistency projection method.
- Integrated multi-view similarity data (circRNA functional, disease semantic, association profile).
- Projected circRNA and disease spaces onto the circRNA-disease interaction network.
Main Results:
- NCPCDA achieved high accuracy in predicting circRNA-disease relationships.
- Achieved AUCs of 0.9541 (LOOCV) and 0.9201 (5-fold CV).
- Case studies demonstrated NCPCDA's potential for discovering new interactions.
Conclusions:
- NCPCDA is an efficient and accurate computational tool for inferring circRNA-disease associations.
- The method shows promise for identifying novel circRNA-disease interactions.
- The findings contribute to understanding disease mechanisms and biomarker discovery.
