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SGCLMD: Signed graph-based contrastive learning model for predicting somatic mutation-drug association.

Xiaosong Wang1, Haisong Feng2, Yilei Zhang3

  • 1School of Information and Artificial Intelligence, Anhui Agricultural University, Hefei, Anhui, 230036, China.

Computers in Biology and Medicine
|March 27, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces SGCLMD, a computational model that predicts somatic mutation-drug associations to advance cancer treatment. It improves upon existing methods for identifying targeted therapies and personalizing cancer care.

Keywords:
Graph neural networkMulti-view contrastive learningSigned graphSomatic mutation-drug association

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Area of Science:

  • Genomics
  • Computational Biology
  • Cancer Research

Background:

  • Somatic mutations drive cancer by affecting cellular processes and leading to uncontrolled growth.
  • Understanding somatic mutation-drug interactions is key to deciphering cancer biology and improving patient outcomes.
  • Personalized medicine relies on identifying specific mutation-drug relationships for targeted therapies.

Purpose of the Study:

  • To develop a computational model for predicting somatic mutation-drug associations.
  • To enhance the understanding of biological mechanisms underlying cancer development and treatment response.
  • To improve the accuracy of identifying targeted therapeutic interventions for cancer patients.

Main Methods:

  • Developed a signed graph comparison learning for mutation-drug associations (SGCLMD) model.
  • Constructed a benchmark dataset of somatic mutation-drug associations from clinical data.
  • Employed a graph enhancement method with random perturbation and a multi-view comparison loss algorithm for node representation learning.

Main Results:

  • The SGCLMD model achieved optimal AUC of 0.8306 and AUPR of 0.8751.
  • Demonstrated a 3% and 3.1% improvement over the state-of-the-art method in AUC and AUPR, respectively.
  • Ablation experiments and case studies validated the model's predictive potential.

Conclusions:

  • SGCLMD effectively predicts somatic mutation-drug associations, offering a valuable tool for cancer research.
  • The model's graph enhancement and multi-view contrast learning modules are crucial for its performance.
  • This work contributes to advancing personalized cancer treatment strategies through improved mutation-drug association predictions.