Last modified by Andrea Omicini on 01/05/2021 16:48

Mukesh Bansal, Vincenzo Belcastro, Alberto Ambesi-Impiombato, Diego di Bernardo

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.

Molecular Systems Biology, February 2007
title = {How to infer gene networks from expression profiles },
number = 3,
month = {February},
doi = {10.1038/msb4100120},
status = {Published},
chapter = 78,
year = 2007,
journal = {Molecular Systems Biology},
author = {Bansal, Mukesh and Belcastro,  Vincenzo and Ambesi-Impiombato,  Alberto and di Bernardo,  Diego}}

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