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Updated: Jul 1, 2026

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

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Published on: October 13, 2023

AGCECDA: attention-guided heterogeneous graph collaborative embedding for circRNA-drug sensitivity association

Chao Cao^1,2, Mengli Li², Maozu Guo³

¹Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, Sichuan, 611731, China.

|June 29, 2026

Summary

This study introduces a novel graph learning framework to predict circular RNA-drug sensitivity. The model effectively integrates diverse data, outperforming existing methods for precision medicine applications.

Keywords:

Attention mechanism CircRNA–drug association prediction Collaborative feature mining Cross-modal feature fusion Graph representation learning Heterogeneous biological networks

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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

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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

Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Circular RNAs (circRNAs) are key regulators in disease and drug response.
Accurate circRNA-drug sensitivity prediction is vital for precision medicine.
Existing methods struggle with integrating semantic and structural data.

Purpose of the Study:

To develop an advanced computational framework for circRNA-drug sensitivity prediction.
To overcome limitations of current methods in feature integration and optimization.
To enhance understanding of therapeutic mechanisms and drug response.

Main Methods:

An end-to-end graph representation learning framework.
Joint modeling of homogeneous similarity and heterogeneous interactions.
Integration of fused similarity graphs, attention-based semantic encoding, and graph convolutional networks.
Cross-modal collaborative feature mining for multi-source representation optimization.

Main Results:

The proposed framework demonstrates superior performance in circRNA-drug sensitivity prediction.
Consistent results across 5-fold/10-fold cross-validation and independent tests.
Validation through ablation and case studies confirms effectiveness.

Conclusions:

The framework offers a robust computational strategy for predicting circRNA-drug sensitivity.
Provides a valuable tool for identifying therapeutic associations.
Facilitates advancements in drug response analysis and precision medicine.