How to infer gene networks from expression profiles. Export

@article{citeulike:1116998, 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.}, author = {Bansal, Mukesh and Belcastro, Vincenzo and Ambesi-Impiombato, Alberto and di Bernardo, Diego}, citeulike-article-id = {1116998}, citeulike-linkout-0 = {http://dx.doi.org/10.1038/msb4100120}, citeulike-linkout-1 = {http://dx.doi.org/10.1038/msb4100120}, citeulike-linkout-2 = {http://view.ncbi.nlm.nih.gov/pubmed/17299415}, citeulike-linkout-3 = {http://www.hubmed.org/display.cgi?uids=17299415}, day = {13}, doi = {10.1038/msb4100120}, issn = {1744-4292}, journal = {Molecular systems biology}, keywords = {gene-expression, gene-networks, gene-regulation, microarray, reverse-engineering}, month = {February}, posted-at = {2009-09-20 23:02:48}, priority = {2}, publisher = {Nature Publishing Group}, title = {How to infer gene networks from expression profiles.}, url = {http://dx.doi.org/10.1038/msb4100120}, volume = {3}, year = {2007} }

TY - JOUR ID - citeulike:1116998 L3 - citeulike-article-id:1116998 N2 - 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. JF - Molecular systems biology SN - 1744-4292 TI - How to infer gene networks from expression profiles. PB - Nature Publishing Group VL - 3 KW - gene-expression KW - gene-networks KW - gene-regulation KW - microarray KW - reverse-engineering AU - Bansal, Mukesh AU - Belcastro, Vincenzo AU - Ambesi-Impiombato, Alberto AU - di Bernardo, Diego PY - 2007/02/13/ UR - http://dx.doi.org/10.1038/msb4100120 ER -