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September 12, 2025ACS OmegaOpen Access

A Multisource Transformer-Guided Graph Representation Learning Framework for circRNA-Disease Association Prediction

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Authors

SLShuai LiangLWLei WangZYZhu‐Hong You

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Overview

The proposed model predicts circRNA-disease associations using a heterogeneous graph network, suggesting improved accuracy.

Key Points

  • MTGCDA achieved an AUC of 0.9756 for predicting circRNA-disease associations, showcasing high accuracy.
  • The model integrates multisource biological information and leverages a heterogeneous graph neural network.
  • Significant experimental validation was performed, with 17 out of 20 predicted associations corroborated by literature.
  • This approach addresses challenges in prediction methods related to information integration and semantic representation.

Cite This Study

Liang et al. (2025) studied this question.

synapsesocial.com/papers/68d44c5531b076d99fa563cehttps://doi.org/10.1021/acsomega.5c06830
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  1. 1Exploring potential circRNA biomarkers for cancers based on double-line heterogeneous graph representation learning2024 · 1 citations
  2. 2A model of multi-view contrastive hypergraph learning for predicting circRNA-disease associations2026
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  4. 4A multi-scale neighbor topology guided transformer and Kolmogorov-Arnold network enhanced feature learning model for disease-related circRNA prediction2025
  5. 5RDGAN: Prediction of circRNA-Disease Associations Via Resistance Distance and Graph Attention Network2024 · 4 citations