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A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

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Predicting circRNA-Disease Associations Based on Improved Collaboration Filtering Recommendation System With Multiple

Xiujuan Lei¹, Zengqiang Fang¹, Ling Guo²

¹School of Computer Science, Shaanxi Normal University, Xi'an, China.

Frontiers in Genetics

|October 15, 2019

Summary

A new computational method, ICFCDA, effectively predicts circular RNA-disease associations using a collaborative filtering system. This approach overcomes the "cold start" problem, offering a faster and more accurate alternative to traditional experimental methods for identifying disease biomarkers.

Keywords:

circRNA–disease association collaboration filtering multiple biological data neighbor information recommendation system

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Area of Science:

Bioinformatics
Genomics
Computational Biology

Background:

Circular RNAs (circRNAs) are novel noncoding RNAs with critical roles in gene regulation and disease diagnosis.
Their unique stable structure makes circRNAs promising biomarkers for various diseases.
Current methods for identifying circRNA-disease associations are often time-consuming and expensive.

Purpose of the Study:

To develop an efficient computational method for predicting potential circRNA-disease associations.
To address the
cold start
problem in circRNA-disease association prediction.
To provide a reliable and cost-effective tool for biomarker discovery.

Main Methods:

Utilized the circR2Disease database for known circRNA-disease associations.
Constructed circRNA and disease similarity networks using data from multiple databases.
Employed a collaborative filtering recommendation system (ICFCDA) for prediction.
Validated the method using leave-one-out cross-validation and case studies.

Main Results:

ICFCDA achieved an Area Under the Curve (AUC) of 0.946, outperforming existing methods.
Case studies confirmed the accuracy of ICFCDA predictions against known disease associations.
The method demonstrated robustness and competence in predicting circRNA-disease relationships.

Conclusions:

ICFCDA offers a powerful computational approach for predicting circRNA-disease associations.
This method provides a valuable tool for accelerating the discovery of circRNA biomarkers for diseases.
The findings highlight the potential of computational strategies in advancing biomedical research.