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Updated: Aug 6, 2026
05:47
Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
Published on: June 13, 2025
Knowledge-driven interpretable neural networks for mechanistic insight
Yan Ke1, Tianwei Yu2
1The Chinese University of Hong Kong.
Genome Research
|July 24, 2026
Summary
This study introduces a novel machine learning framework for pathway analysis, enhancing the understanding of molecular mechanisms in diseases like breast cancer and COVID-19 by integrating omics data with biological pathways.
Area of Science:
- Bioinformatics
- Systems Biology
- Machine Learning
Background:
- Pathway analysis is crucial for understanding disease mechanisms but current methods lack mechanistic detail.
- Existing approaches often oversimplify complex biological interactions, limiting deep insights.
Purpose of the Study:
- To develop a knowledge-driven machine learning framework for in-depth pathway analysis.
- To enable interpretable modeling of biological interactions using omics data.
- To bridge predictive modeling with mechanistic interpretation for disease research.
Main Methods:
- A knowledge-driven machine learning framework embedding features into pathway graphs.
- Analytical modeling of biological reactions within pathways.
- Estimation of functional associations to model biological interactions.
- Feature selection agnostic approach utilizing full omics datasets.
Main Results:
- Generated interpretable feature hierarchies and subnetworks.
- Successfully applied to breast cancer microRNA-gene regulation and COVID-19 metabolomics data.
- Highlighted key immune and metabolic pathways associated with disease progression.
Conclusions:
- The framework provides a foundation for integrative pathway analysis, combining predictive power with mechanistic understanding.
- Enables deeper insights into molecular mechanisms underlying pathological changes.
- Offers a robust method for analyzing complex omics data in the context of biological pathways.
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