title: How to infer gene networks from expression profiles.
abstract: Inferring, or 'reverse-engineering', gene networks can be defined as the process of identifying gene interactions from experimental data through computational analysis. Gene expression data from microarrays are typically used for this purpose. Here we compared different reverse-engineering algorithms for which ready-to-use software was available and that had been tested on experimental data sets. We show that reverse-engineering algorithms are indeed able to correctly infer regulatory interactions among genes, at least when one performs perturbation experiments complying with the algorithm requirements. These algorithms are superior to classic clustering algorithms for the purpose of finding regulatory interactions among genes, and, although further improvements are needed, have reached a discreet performance for being practically useful.
authors.affiliation: Telethon Institute of Genetics and Medicine, Via P Castellino, Naples, Italy.
authors.forename: Mukesh
authors.initials: M
authors.lastname: Bansal
authors.forename: Vincenzo
authors.initials: V
authors.lastname: Belcastro
authors.forename: Alberto
authors.initials: A
authors.lastname: Ambesi-Impiombato
authors.forename: Diego
authors.initials: D
authors.lastname: di Bernardo
mesh_terms: D000465:Algorithms; D019295:Computational Biology; D020869:Gene Expression Profiling / Q000379:methods*; D005786:Gene Expression Regulation; D053263:Gene Regulatory Networks; D020411:Oligonucleotide Array Sequence Analysis; D012984:Software; D049490:Systems Biology / Q000379:methods*