title: A novel procedure for statistical inference and verification of gene regulatory subnetwork.
abstract: BACKGROUND
  The reconstruction of gene regulatory network from time course microarray data can help us comprehensively understand the biological system and discover the pathogenesis of cancer and other diseases. But how to correctly and efficiently decifer the gene regulatory network from high-throughput gene expression data is a big challenge due to the relatively small amount of observations and curse of dimensionality. Computational biologists have developed many statistical inference and machine learning algorithms to analyze the microarray data. In the previous studies, the correctness of an inferred regulatory network is manually checked through comparing with public database or an existing model.
  
  
  RESULTS
  In this work, we present a novel procedure to automatically infer and verify gene regulatory networks from time series expression data. The dynamic Bayesian network, a statistical inference algorithm, is at first implemented to infer an optimal network from time series microarray data of S. cerevisiae, then, a weighted symbolic model checker is applied to automatically verify or falsify the inferred network through checking some desired temporal logic formulas abstracted from experiments or public database.
  
  
  CONCLUSIONS
  Our studies show that the marriage of statistical inference algorithm with model checking technique provides a more efficient way to automatically infer and verify the gene regulatory network from time series expression data than previous studies.
authors.forename: Haijun
authors.initials: H
authors.lastname: Gong
authors.forename: Jakob
authors.initials: J
authors.lastname: Klinger
authors.forename: Kevin
authors.initials: K
authors.lastname: Damazyn
authors.forename: Xiangrui
authors.initials: X
authors.lastname: Li
authors.forename: Shiyang
authors.initials: S
authors.lastname: Huang
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D003627:Data Interpretation, Statistical; D020869:Gene Expression Profiling; D015966:Gene Expression Regulation, Fungal / Q000235:genetics*; D053263:Gene Regulatory Networks; D016681:Genome, Fungal; D006801:Humans; D020411:Oligonucleotide Array Sequence Analysis; D012441:Saccharomyces cerevisiae / Q000235:genetics*; D012984:Software; D013997:Time Factors