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Related Experiment Video

Updated: Aug 24, 2025

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
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DRGCNCDA: Predicting circRNA-disease interactions based on knowledge graph and disentangled relational graph

Wei Lan1, Hongyu Zhang2, Yi Dong2

  • 1School of Computer, Electronic and Information, Guangxi University, Nanning, China; Guangxi Key Laboratory of Multimedia Communications and Network Technology, Guangxi University, Nanning, China.

Methods (San Diego, Calif.)
|October 24, 2022
PubMed
Summary

A new computational model, DRGCNCDA, enhances prediction of circular RNA (circRNA) and disease associations by integrating multiple biological relationships. This method outperforms existing models, offering reliable candidates for future biological experiments.

Keywords:
Disentangled graph convolutional networkKnowledge graphcircRNA-disease association

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

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Circular RNAs (circRNAs) are increasingly recognized for their roles in human disease diagnosis and prognosis.
  • Existing computational methods for predicting circRNA-disease associations often overlook interactions with other biological entities and latent factors.

Purpose of the Study:

  • To propose a novel computational model, DRGCNCDA, to address limitations in current circRNA-disease association prediction.
  • To improve the accuracy and comprehensiveness of predicting circRNA-disease associations by incorporating multi-relational data.

Main Methods:

  • Constructed multi-relational graphs integrating circRNA, disease, miRNA, and lncRNA data.
  • Employed a disentangled relational graph convolutional network (DRGCNCDA) to derive feature vectors for circRNAs and diseases.
  • Utilized a knowledge graph model to predict circRNA-disease association affinity scores based on learned embeddings.

Main Results:

  • The DRGCNCDA model demonstrated superior performance compared to existing computational methods.
  • Experimental validation using 5-fold cross-validation confirmed the model's effectiveness.
  • A case study highlighted the model's capability in predicting circRNA-disease associations and identifying potential candidates for biological experiments.

Conclusions:

  • The DRGCNCDA model offers a significant advancement in predicting circRNA-disease associations.
  • The integration of multi-relational data and advanced network techniques enhances prediction accuracy.
  • DRGCNCDA provides a valuable tool for biological research, aiding in the identification of potential disease-related circRNAs.