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Related Experiment Video

Updated: Dec 21, 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

GCNCDA: A new method for predicting circRNA-disease associations based on Graph Convolutional Network Algorithm.

Lei Wang^1,2, Zhu-Hong You², Yang-Ming Li³

¹College of Information Science and Engineering, Zaozhuang University, Zaozhuang, China.

Plos Computational Biology

|May 21, 2020

Summary

This summary is machine-generated.

This study introduces GCNCDA, a computational method using Graph Convolutional Networks to predict circular RNA-disease associations. GCNCDA effectively identifies potential links, aiding disease research and diagnosis.

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Last Updated: Dec 21, 2025

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Identification of Circular RNAs using RNA Sequencing

Identification of Circular RNAs using RNA Sequencing

Published on: November 14, 2019

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Circular RNAs (circRNAs) are implicated in various diseases, making their association identification crucial for understanding pathogenesis and improving diagnostics.
Experimental identification of circRNA-disease associations is costly and time-consuming due to complex underlying mechanisms.

Purpose of the Study:

To develop and validate a computational method, GCNCDA, for predicting novel circRNA-disease associations.
To leverage deep learning, specifically Graph Convolutional Networks (GCNs), for efficient and accurate prediction.

Main Methods:

GCNCDA integrates disease semantic similarity and Gaussian Interaction Profile (GIP) kernel similarity into a unified descriptor.
The FastGCN algorithm extracts high-level features from the fused descriptor.
The Forest PA classifier predicts potential circRNA-disease associations.

Main Results:

GCNCDA achieved 91.2% accuracy, 92.78% sensitivity, and an AUC of 90.90% on the circR2Disease dataset.
Case studies on breast cancer, glioma, and colorectal cancer confirmed a high percentage of top predicted circRNA-disease associations in existing literature.
GCNCDA demonstrated superior performance compared to other state-of-the-art methods.

Conclusions:

GCNCDA is a competitive and effective computational tool for predicting circRNA-disease associations.
The method provides reliable candidates for further experimental validation, advancing disease research.