title: A novel parametric approach to mine gene regulatory relationship from microarray datasets.
abstract: BACKGROUND
  Microarray has been widely used to measure the gene expression level on the genome scale in the current decade. Many algorithms have been developed to reconstruct gene regulatory networks based on microarray data. Unfortunately, most of these models and algorithms focus on global properties of the expression of genes in regulatory networks. And few of them are able to offer intuitive parameters. We wonder whether some simple but basic characteristics of microarray datasets can be found to identify the potential gene regulatory relationship.
  
  
  RESULTS
  Based on expression correlation, expression level variation and vectors derived from microarray expression levels, we first introduced several novel parameters to measure the characters of regulating gene pairs. Subsequently, we used the naïve Bayesian network to integrate these features as well as the functional co-annotation between transcription factors and their target genes. Then, based on the character of time-delay from the expression profile, we were able to predict the existence and direction of the regulatory relationship respectively.
  
  
  CONCLUSIONS
  Several novel parameters have been proposed and integrated to identify the regulatory relationship. This new model is proved to be of higher efficacy than that of individual features. It is believed that our parametric approach can serve as a fast approach for regulatory relationship mining.
authors.affiliation: State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, Beijing 102206, China. wanlinliu@gmail.com
authors.forename: Wanlin
authors.initials: W
authors.lastname: Liu
authors.forename: Dong
authors.initials: D
authors.lastname: Li
authors.forename: Qijun
authors.initials: Q
authors.lastname: Liu
authors.forename: Yunping
authors.initials: Y
authors.lastname: Zhu
authors.forename: Fuchu
authors.initials: F
authors.lastname: He
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D057225:Data Mining; D015870:Gene Expression; D020869:Gene Expression Profiling / Q000379:methods*; D053263:Gene Regulatory Networks; D016013:Likelihood Functions; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D014157:Transcription Factors / Q000378:metabolism
chemical_list: D014157:Transcription Factors