title: Sample scale-free gene regulatory network using gene ontology.
abstract: Currently there are various approaches to the reconstruction of gene regulatory networks from different sources of data. However, none of these methods incorporates explicitly scale-free property, one of the most important features of the targeted network, into their algorithms. In this paper, several network sampling strategies are explored on a set assembled from previous published gene interactions in yeast, expecting to reconstruct regulatory networks that are scale-free.
authors.affiliation: Department of Computer Science, University of Illinois at 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; D019295:Computational Biology / Q000295:instrumentation / Q000379:methods*; D003198:Computer Simulation; D020869:Gene Expression Profiling; D015966:Gene Expression Regulation, Fungal; D053263:Gene Regulatory Networks; D005800:Genes, Fungal; D006801:Humans; D008957:Models, Genetic; D015233:Models, Statistical; D020411:Oligonucleotide Array Sequence Analysis; D015203:Reproducibility of Results; D012441:Saccharomyces cerevisiae / Q000235:genetics / Q000378:metabolism; D012984:Software