2023/06/25 by J. Smith, Smith, J., Andriëtte Bekker +3
Biochemistry, Genetics and Molecular Biology · Computer Science · #Bioinformatics and Genomic Networks #FOS: Computer and information sciences #Gene expression and cancer classification #Methodology (stat.ME) #Neural Networks and Applications
paper · pdf · doi:10.48550/arxiv.2306.14199
openalex publication_date 2023/06/25 · openalex created_date 2023/06/28 · openalex updated_date 2026/07/28
Differential Networks (DNs), tools that encapsulate interactions within intricate systems, are brought under the Bayesian lens in this research. A novel naıve Bayesian adaptive graphical elastic net (BAE) prior is introduced to estimate the components of the DN. A heuristic structure determination mechanism and a block Gibbs sampler are derived. Performance is initially gauged on synthetic datasets encompassing various network topologies, aiming to assess and compare the flexibility to those of the Bayesian adaptive graphical lasso and ridge-type procedures. The naıve BAE estimator consistently ranks within the top two performers, highlighting its inherent adaptability. Finally, the BAE is applied to real-world datasets across diverse domains such as oncology, nephrology, and enology, underscoring its potential utility in comprehensive network analysis.