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Knowledge-guided Bayesian biclustering model for omics data with noisy graphs
Qiyiwen Zhang1, Wenrui Li2, Suprateek Kundu3
1Department of Medicine, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania 15213, United States.
Biometrics
|May 18, 2026
Summary
This study introduces a novel Bayesian biclustering method to improve disease subtyping by integrating noisy biological network data. The approach effectively handles false positive and false negative edges for more accurate biological insights.
Area of Science:
- Bioinformatics
- Computational Biology
- Systems Biology
Background:
- High-dimensional omics data analysis presents challenges in biomedical research.
- Disease subtyping is crucial for personalized medicine, diagnosis, and treatment.
- Biclustering is a key statistical method for disease subtyping.
Purpose of the Study:
- To develop a robust biclustering method that integrates noisy biological graph knowledge.
- To address limitations of existing methods in handling false positive and false negative graph edges.
- To enhance the accuracy and interpretability of biclustering for disease subtyping.
Main Methods:
- A Bayesian denoising knowledge-guided biclustering approach is proposed.
- Multiple biological graphs are integrated and de-noised by modeling false positive/negative errors.
- A Markov chain Monte Carlo algorithm is utilized for bicluster estimation.
Main Results:
- The proposed method effectively handles noisy biological graphs.
- Simulations and real-world data analyses demonstrate superior performance.
- Accurate biclusters were identified from gene expression and proteomics data.
Conclusions:
- The Bayesian denoising method offers a significant advancement in graph-guided biclustering.
- This approach enhances disease subtyping by robustly integrating biological network information.
- The method provides valuable biological insights for complex diseases like Alzheimer's.
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