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MDAGCN: Predicting Mutation-Drug Associations Through Signed Graph Convolutional Networks via Graph Sampling

IEEE Transactions on Computational Biology and Bioinformatics +

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August 14, 2025

|

August 14, 2025

View abstract on PubMed

Summary

This summary is machine-generated.

We developed MDAGCN, a graph convolutional network, to predict cancer mutation-drug associations for precision medicine. This method accurately identifies drug sensitivity or resistance, aiding cancer treatment and drug development.

Area Of Science

  • Computational biology
  • Genomics
  • Pharmacogenomics

Background

  • High-throughput molecular data in cancer precision medicine poses computational challenges.
  • Genetic mutations can serve as biomarkers for predicting targeted drug responses.
  • Accurate prediction of mutation-drug associations is crucial for cancer therapy and drug discovery.

Purpose Of The Study

  • To propose a novel graph convolutional network method, MDAGCN, for predicting mutation-drug associations (sensitivity/resistance) in cancer.
  • To enhance the efficiency and accuracy of computational models for mutation-drug interaction prediction.

Main Methods

  • Constructing a feature and topological graph using the k-Nearest Neighbors algorithm.
  • Incorporating structural relationships and feature data of mutation-drug interactions.
  • Utilizing a graph convolutional network (MDAGCN) for prediction.
  • Employing a graph sampling technique for training signed graphs.

Main Results

  • MDAGCN demonstrates superior performance compared to state-of-the-art methods in predicting mutation-drug associations.
  • The effectiveness of the graph sampling technique for training signed graphs was validated.
  • The model accurately predicts drug sensitivity and resistance.

Conclusions

  • MDAGCN provides a comprehensive end-to-end framework for cancer pharmacogenomics.
  • The framework facilitates the discovery of novel mutation-drug associations.
  • It aids in the in-depth analysis of drug sensitivity and resistance in cancer treatment.