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SAGCN: Using Graph Convolutional Network With Subgraph-Aware for circRNA-Drug Sensitivity Identification.

Weicheng Sun, Chengjuan Ren, Jinsheng Xu

IEEE/ACM Transactions on Computational Biology and Bioinformatics

|June 17, 2024

Summary

This study introduces SAGCN, a computational method for predicting circular RNA-drug sensitivity associations (CDA). SAGCN accurately identifies these crucial links, aiding cancer therapy and drug discovery.

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Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Circular RNAs (circRNAs) are implicated in cancer development and drug resistance.
circRNA expression significantly influences cellular drug sensitivity.
Identifying circRNA-drug sensitivity associations (CDA) is vital for advancing cancer treatment and drug discovery.

Purpose of the Study:

To address the time-consuming and costly nature of experimental CDA identification.
To develop an efficient computational method for predicting CDA.
To propose the subgraph-aware graph convolutional network (SAGCN) for CDA prediction.

Main Methods:

Constructed a heterogeneous network integrating circRNA similarity, drug similarity, and circRNA-drug bipartite networks.
Employed a subgraph extractor utilizing graph convolutional networks to learn latent network structures.
Integrated 1-hop and 2-hop information using a fusing attention mechanism and a novel subgraph-aware attention mechanism.

Main Results:

The SAGCN model achieved an average AUC of 0.9120 and AUPR of 0.8693.
SAGCN outperformed existing state-of-the-art models in 10-fold cross-validation.
Case studies validated SAGCN's capability in identifying circRNA-drug sensitivity links.

Conclusions:

SAGCN provides an effective computational approach for predicting circRNA-drug sensitivity associations.
The method offers a valuable tool for accelerating drug discovery and optimizing cancer therapies.
SAGCN's performance highlights the potential of graph-based deep learning in biological network analysis.