title: Computational inference and analysis of genetic regulatory networks via a supervised combinatorial-optimization pattern.
abstract: BACKGROUND
  Post-genome era brings about diverse categories of omics data. Inference and analysis of genetic regulatory networks act prominently in extracting inherent mechanisms, discovering and interpreting the related biological nature and living principles beneath mazy phenomena, and eventually promoting the well-beings of humankind.
  
  
  RESULTS
  A supervised combinatorial-optimization pattern based on information and signal-processing theories is introduced into the inference and analysis of genetic regulatory networks. An associativity measure is proposed to define the regulatory strength/connectivity, and a phase-shift metric determines regulatory directions among components of the reconstructed networks. Thus, it solves the undirected regulatory problems arising from most of current linear/nonlinear relevance methods. In case of computational and topological redundancy, we constrain the classified group size of pair candidates within a multiobjective combinatorial optimization (MOCO) pattern.
  
  
  CONCLUSIONS
  We testify the proposed approach on two real-world microarray datasets of different statistical characteristics. Thus, we reveal the inherent design mechanisms for genetic networks by quantitative means, facilitating further theoretic analysis and experimental design with diverse research purposes. Qualitative comparisons with other methods and certain related focuses needing further work are illustrated within the discussion section.
authors.affiliation: Department of Bioinformatics, Tongji University, Shanghai, China. bme.tang@gmail.com
authors.forename: Binhua
authors.initials: B
authors.lastname: Tang
authors.forename: Xuechen
authors.initials: X
authors.lastname: Wu
authors.forename: Ge
authors.initials: G
authors.lastname: Tan
authors.forename: Su-Shing
authors.initials: SS
authors.lastname: Chen
authors.forename: Qing
authors.initials: Q
authors.lastname: Jing
authors.forename: Bairong
authors.initials: B
authors.lastname: Shen
mesh_terms: D002453:Cell Cycle; D019295:Computational Biology; D003627:Data Interpretation, Statistical; D020869:Gene Expression Profiling; D053263:Gene Regulatory Networks; D008954:Models, Biological; D015233:Models, Statistical; D020411:Oligonucleotide Array Sequence Analysis