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Related Experiment Videos

Interpretable graph-based models on multimodal biomedical data integration: a technical review and benchmarking.

Alireza Sadeghi1, Farshid Hajati2, Ahmadreza Argha3,4

  • 1Holcombe Department of Electrical and Computer Engineering, Clemson University, Clemson, SC, USA.

Nature Communications
|June 16, 2026
PubMed
Summary

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This summary is machine-generated.

Interpretable graph models enhance multimodal biomedical data analysis for disease classification. Benchmarking explainable AI (XAI) methods reveals complementary strengths for trustworthy AI in healthcare.

Area of Science:

  • Biomedical Informatics
  • Artificial Intelligence
  • Data Science

Background:

  • Integrating diverse biomedical data is crucial for healthcare insights.
  • Graph-based models excel at capturing complex relationships in data.
  • Clinical adoption of these models requires high interpretability.

Purpose of the Study:

  • To review interpretable graph-based models for multimodal biomedical data.
  • To highlight trends in disease classification, graph construction, and explainability.
  • To benchmark explainable artificial intelligence (XAI) techniques.

Main Methods:

  • Survey of interpretable graph-based models in biomedical research.
  • Categorization of XAI techniques for graph analysis.
  • Benchmarking of SHAP, saliency, sensitivity, and graph masking on Alzheimer's disease data.

Related Experiment Videos

Main Results:

  • Identified dominant trends in disease classification and static graph construction.
  • Demonstrated complementary strengths of various XAI techniques.
  • Provided a benchmark for explainability methods on real-world biomedical data.

Conclusions:

  • Interpretable graph models are vital for trustworthy biomedical AI.
  • XAI methods offer valuable insights into model predictions.
  • Future work should explore dynamic graphs and LLM-based explainability.