2007/06/03 by Edoardo M. Airoldi, Edoardo M Airoldi, David M. Blei +9 · 1 citation
Biochemistry, Genetics and Molecular Biology · Physics and Astronomy · Psychology · #Bioinformatics and Genomic Networks #Complex Network Analysis Techniques #FOS: Biological sciences #Mental Health Research Topics #Molecular Networks (q-bio.MN) #Quantitative Methods (q-bio.QM) #q-bio.MN #q-bio.QM
paper · pdf · doi:10.48550/arxiv.0706.0294
22 pages, 6 figures, 2 tables
openalex publication_date 2007/06/03 · arxiv created 2007/11/15 · arxiv updated 2009/12/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In this paper, we consider the statistical analysis of a protein interaction network. We propose a Bayesian model that uses a hierarchy of probabilistic assumptions about the way proteins interact with one another in order to: (i) identify the number of non-observable functional modules; (ii) estimate the degree of membership of proteins to modules; and (iii) estimate typical interaction patterns among the functional modules themselves. Our model describes large amount of (relational) data using a relatively small set of parameters that we can reliably estimate with an efficient inference algorithm. We apply our methodology to data on protein-to-protein interactions in saccharomyces cerevisiae to reveal proteins' diverse functional roles. The case study provides the basis for an overview of which scientific questions can be addressed using our methods, and for a discussion of technical issues.