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September 10, 2025IEEE Journal of Biomedical and Health Informatics

A multi-scale neighbor topology guided transformer and Kolmogorov-Arnold network enhanced feature learning model for disease-related circRNA prediction

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Authors

PXPing XuanHLHaijiang LiHCHui Cui

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Overview

Proposed model improves circRNA-disease predictions using multi-scale neighbor topology and transformers.

Key Points

  • MKCD enhances prediction accuracy for disease-related circRNAs, improving AUC by at least 14.1%.
  • The model utilizes a multi-scale neighbor topology to better capture relationships among circRNA, miRNA, and diseases.
  • A feature-gated network evaluates the importance of topological features from the multi-scale transformation process.
  • Case studies demonstrated the model's ability to identify reliable circRNA candidates for various diseases.

Cite This Study

Xuan et al. (2025) studied this question.

synapsesocial.com/papers/68c1cc2e54b1d3bfb60f430bhttps://doi.org/10.1109/jbhi.2025.3600406
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1A Multisource Transformer-Guided Graph Representation Learning Framework for circRNA-Disease Association Prediction2025
  2. 2MSGTrans: circRNA-disease association prediction with cluster-based negative sampling and multi-scale graph transformer2026
  3. 3MAMLCDA: A Meta-Learning Model for Predicting circRNA-Disease Association Based on MAML Combined With CNN2024 · 10 citations
  4. 4Enhancing lncRNA-Disease Association Prediction Through Collaborative Representation of Multi-Source Heterogeneous Features2026
  5. 5LGCDA: Predicting CircRNA-Disease Association Based on Fusion of Local and Global Features2024 · 19 citations