title: Modular analysis of the probabilistic genetic interaction network.
abstract: BACKGROUND
  Epistatic Miniarray Profiles (EMAP) has enabled the mapping of large-scale genetic interaction networks; however, the quantitative information gained from EMAP cannot be fully exploited since the data are usually interpreted as a discrete network based on an arbitrary hard threshold. To address such limitations, we adopted a mixture modeling procedure to construct a probabilistic genetic interaction network and then implemented a Bayesian approach to identify densely interacting modules in the probabilistic network.
  
  
  RESULTS
  Mixture modeling has been demonstrated as an effective soft-threshold technique of EMAP measures. The Bayesian approach was applied to an EMAP dataset studying the early secretory pathway in Saccharomyces cerevisiae. Twenty-seven modules were identified, and 14 of those were enriched by gold standard functional gene sets. We also conducted a detailed comparison with state-of-the-art algorithms, hierarchical cluster and Markov clustering. The experimental results show that the Bayesian approach outperforms others in efficiently recovering biologically significant modules.
authors.affiliation: School of Mathematical Sciences, Peking University, Beijing 100871, China.
authors.forename: Lin
authors.initials: L
authors.lastname: Hou
authors.forename: Lin
authors.initials: L
authors.lastname: Wang
authors.forename: Minping
authors.initials: M
authors.lastname: Qian
authors.forename: Dong
authors.initials: D
authors.lastname: Li
authors.forename: Chao
authors.initials: C
authors.lastname: Tang
authors.forename: Yunping
authors.initials: Y
authors.lastname: Zhu
authors.forename: Minghua
authors.initials: M
authors.lastname: Deng
authors.forename: Fangting
authors.initials: F
authors.lastname: Li
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods; D053263:Gene Regulatory Networks; D008390:Markov Chains; D008957:Models, Genetic; D010766:Phosphorylation; D012441:Saccharomyces cerevisiae / Q000235:genetics / Q000378:metabolism; D029701:Saccharomyces cerevisiae Proteins / Q000235:genetics / Q000378:metabolism
chemical_list: D029701:Saccharomyces cerevisiae Proteins