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Adaptive Bayesian variable clustering via structural learning of breast cancer data.

Riddhi Pratim Ghosh1, Arnab K Maity2, Mohsen Pourahmadi3

  • 1Department of Mathematics and Statistics, Bowling Green State University, Bowling Green, Ohio, USA.

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This study introduces a Bayesian model for clustering proteins based on their correlations. The method effectively identifies protein clusters and their numbers, crucial for understanding cancer cell biology.

Keywords:
Bayesian clusteringangular reparameterizationpathwaysreversible jump Markov chain Monte Carlo

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Statistical Genetics

Background:

  • Protein clustering is vital for understanding cancer cell biology and molecular pathways.
  • Existing methods may lack flexibility in simultaneously estimating cluster number and configuration.

Purpose of the Study:

  • To propose a novel hierarchical Bayesian model for protein clustering.
  • To effectively cluster proteins based on correlation structure and estimate the number of clusters.

Main Methods:

  • A hierarchical Bayesian model utilizing a multivariate normal likelihood.
  • Angle-based correlation reparameterization and a truncated Poisson prior for the number of clusters.
  • Reversible jump Markov chain Monte Carlo (RJMCMC) for posterior simulation.

Main Results:

  • The proposed Bayesian method successfully clusters proteins and estimates the number of clusters.
  • Demonstrated flexibility in handling complex correlation structures.
  • Validated through extensive simulations and a real-world breast cancer dataset.

Conclusions:

  • The developed Bayesian approach offers a robust and flexible tool for protein clustering in biological research.
  • This method aids in deciphering protein interactions and their roles in diseases like cancer.