Open AccessHow to infer gene networks from expression profiles
Author(s) -
Bansal Mukesh,
Belcastro Vincenzo,
AmbesiImpiombato Alberto,
di Bernardo Diego
Publication year - 2007
Publication title -
molecular systems biology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 8.523
H-Index - 148
ISSN - 1744-4292
DOI - 10.1038/msb4100120
Subject(s) - reverse engineering , biology , gene regulatory network , cluster analysis , dna microarray , computational biology , gene expression profiling , data mining , gene expression , gene , software , systems biology , computer science , bioinformatics , genetics , machine learning , programming language
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.