title: Rank-based edge reconstruction for scale-free genetic regulatory networks.
abstract: BACKGROUND
  The reconstruction of genetic regulatory networks from microarray gene expression data has been a challenging task in bioinformatics. Various approaches to this problem have been proposed, however, they do not take into account the topological characteristics of the targeted networks while reconstructing them.
  
  
  RESULTS
  In this study, an algorithm that explores the scale-free topology of networks was proposed based on the modification of a rank-based algorithm for network reconstruction. The new algorithm was evaluated with the use of both simulated and microarray gene expression data. The results demonstrated that the proposed algorithm outperforms the original rank-based algorithm. In addition, in comparison with the Bayesian Network approach, the results show that the proposed algorithm gives much better recovery of the underlying network when sample size is much smaller relative to the number of genes.
  
  
  CONCLUSIONS
  The proposed algorithm is expected to be useful in the reconstruction of biological networks whose degree distributions follow the scale-free topology.
authors.affiliation: Department of Computer Science (MC152), University of Illinois at Chicago, 851 South Morgan Street, Chicago, IL 60607, USA. gchen4@uic.edu
authors.forename: Guanrao
authors.initials: G
authors.lastname: Chen
authors.forename: Peter
authors.initials: P
authors.lastname: Larsen
authors.forename: Eyad
authors.initials: E
authors.lastname: Almasri
authors.forename: Yang
authors.initials: Y
authors.lastname: Dai
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D020869:Gene Expression Profiling / Q000379:methods*; D005786:Gene Expression Regulation / Q000502:physiology*; D008954:Models, Biological; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D020543:Proteome / Q000378:metabolism*; D015398:Signal Transduction / Q000502:physiology*
chemical_list: D020543:Proteome