Artificial gene networks for objective comparison of analysis algorithms Export

@article{citeulike:49, abstract = {Motivation: Large-scale gene expression profiling generates data sets that are rich in observed features but poor in numbers of observations. The analysis of such data sets is a challenge that has been object of vigorous research. The algorithms in use for this purpose have been poorly documented and rarely compared objectively, posing a problem of uncertainty about the outcomes of the analyses. One way to objectively test such analysis algorithms is to apply them on computational gene network models for which the mechanisms are completely know. Results: We present a system that generates random artificial gene networks according to well-defined topological and kinetic properties. These are used to run in silico experiments simulating real laboratory microarray experiments. Noise with controlled properties is added to the simulation results several times emulating measurement replicates, before expression ratios are calculated. Availability: The data sets and kinetic models described here are available from http://www.vbi.vt.edu/\~{}mendes/AGN/as biochemical dynamic models in SBML and Gepasi formats. Contact: mendes@vt.edu 10.1093/bioinformatics/btg1069}, address = {Virginia Bioinformatics Institute, USA Statistics Department, Virginia Polytechnic Institute and State University, 1880 Pratt Drive, Blacksburg, VA 24061, USA.}, author = {Mendes, Pedro and Sha, Wei and Ye, Keying}, booktitle = {Bioinformatics}, citeulike-article-id = {49}, citeulike-linkout-0 = {http://dx.doi.org/10.1093/bioinformatics/btg1069}, citeulike-linkout-1 = {http://bioinformatics.oxfordjournals.org/content/19/suppl_2/ii122.abstract}, citeulike-linkout-2 = {http://bioinformatics.oxfordjournals.org/content/19/suppl_2/ii122.full.pdf}, citeulike-linkout-3 = {http://bioinformatics.oxfordjournals.org/cgi/content/abstract/19/suppl_2/ii122}, citeulike-linkout-4 = {http://view.ncbi.nlm.nih.gov/pubmed/14534181}, citeulike-linkout-5 = {http://www.hubmed.org/display.cgi?uids=14534181}, day = {27}, doi = {10.1093/bioinformatics/btg1069}, issn = {1367-4803}, journal = {Bioinformatics}, keywords = {artificial, cov\_matrix, mendes, training}, month = {September}, number = {suppl\_2}, pages = {ii122--129}, posted-at = {2004-11-04 02:30:54}, title = {Artificial gene networks for objective comparison of analysis algorithms}, url = {http://dx.doi.org/10.1093/bioinformatics/btg1069}, volume = {19}, year = {2003} }

TY - JOUR ID - citeulike:49 L3 - citeulike-article-id:49 N2 - Motivation: Large-scale gene expression profiling generates data sets that are rich in observed features but poor in numbers of observations. The analysis of such data sets is a challenge that has been object of vigorous research. The algorithms in use for this purpose have been poorly documented and rarely compared objectively, posing a problem of uncertainty about the outcomes of the analyses. One way to objectively test such analysis algorithms is to apply them on computational gene network models for which the mechanisms are completely know. Results: We present a system that generates random artificial gene networks according to well-defined topological and kinetic properties. These are used to run in silico experiments simulating real laboratory microarray experiments. Noise with controlled properties is added to the simulation results several times emulating measurement replicates, before expression ratios are calculated. Availability: The data sets and kinetic models described here are available from http://www.vbi.vt.edu/~mendes/AGN/as biochemical dynamic models in SBML and Gepasi formats. Contact: mendes@vt.edu 10.1093/bioinformatics/btg1069 IS - suppl_2 JF - Bioinformatics SN - 1367-4803 AD - Virginia Bioinformatics Institute, USA Statistics Department, Virginia Polytechnic Institute and State University, 1880 Pratt Drive, Blacksburg, VA 24061, USA. EP - 129 TI - Artificial gene networks for objective comparison of analysis algorithms VL - 19 T2 - Bioinformatics SP - ii122 KW - artificial KW - cov_matrix KW - mendes KW - training AU - Mendes, Pedro AU - Sha, Wei AU - Ye, Keying PY - 2003/09/27/ UR - http://dx.doi.org/10.1093/bioinformatics/btg1069 ER -