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Updated: Feb 3, 2026

In Silico Identification and Characterization of circRNAs During Host-Pathogen Interactions
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PWCDA: Path Weighted Method for Predicting circRNA-Disease Associations.

Xiujuan Lei1, Zengqiang Fang2, Luonan Chen3,4,5

  • 1School of Computer Science, Shaanxi Normal University, Xi'an 710119, China. xjlei@snnu.edu.cn.

International Journal of Molecular Sciences
|November 3, 2018
PubMed
Summary

This study introduces PWCDA, a computational method to predict circular RNA (circRNA)-disease associations. PWCDA utilizes a heterogeneous network and path weighting to identify potential links, offering a cost-effective alternative to experimental methods.

Keywords:
circRNA-disease associationsheterogeneous networkpathway

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Circular RNAs (circRNAs) play significant roles in various diseases.
  • Experimental identification of circRNA-disease associations is time-consuming and expensive.
  • Computational methods are needed for efficient prediction of these associations.

Purpose of the Study:

  • To develop a novel computational path-weighted method for predicting circRNA-disease associations.
  • To integrate multiple similarity measures for enhanced prediction accuracy.
  • To provide a reliable computational tool for circRNA-disease association discovery.

Main Methods:

  • Calculated disease functional similarity and circRNA semantic similarity.
  • Employed Gaussian Interaction Profile (GIP) kernel similarity to address missing data.
  • Constructed a heterogeneous network integrating disease, circRNA, and association data.
  • Computed association scores based on path weighting within the heterogeneous network.

Main Results:

  • The proposed method, PWCDA, demonstrated high reliability and usefulness in predicting circRNA-disease associations.
  • Leave-One-Out Cross-Validation (LOOCV) and five-fold cross-validations confirmed the method's performance.
  • Case studies on Breast Cancer, Gastric Cancer, and Colorectal Cancer highlighted the method's practical applicability.

Conclusions:

  • PWCDA effectively predicts potential circRNA-disease associations.
  • The computational approach offers a valuable alternative to traditional experimental methods.
  • This method can accelerate the discovery of circRNA's role in disease pathogenesis.