Module networks revisited: computational assessment and prioritization of model predictions Export

@article{citeulike:4044418, abstract = {Motivation: The solution of high-dimensional inference and prediction problems in computational biology is almost always a compromise between mathematical theory and practical constraints, such as limited computational resources. As time progresses, computational power increases but well-established inference methods often remain locked in their initial suboptimal solution. Results: We revisit the approach of Segal et al. to infer regulatory modules and their condition-specific regulators from gene expression data. In contrast to their direct optimization-based solution, we use a more representative centroid-like solution extracted from an ensemble of possible statistical models to explain the data. The ensemble method automatically selects a subset of most informative genes and builds a quantitatively better model for them. Genes which cluster together in the majority of models produce functionally more coherent modules. Regulators which are consistently assigned to a module are more often supported by literature, but a single model always contains many regulator assignments not supported by the ensemble. Reliably detecting condition-specific or combinatorial regulation is particularly hard in a single optimum but can be achieved using ensemble averaging. Availability: All software developed for this study is available from http://bioinformatics.psb.ugent.be/software. Contact: tom.michoel@psb.ugent.be Supplementary information: Supplementary data and figures are available from http://bioinformatics.psb.ugent.be/supplementary\_data/anjos/module\_nets\_yeast/. 10.1093/bioinformatics/btn658}, author = {Joshi, Anagha and De Smet, Riet and Marchal, Kathleen and Van de Peer, Yves and Michoel, Tom}, citeulike-article-id = {4044418}, citeulike-linkout-0 = {http://dx.doi.org/10.1093/bioinformatics/btn658}, citeulike-linkout-1 = {http://bioinformatics.oxfordjournals.org/cgi/content/abstract/25/4/490?etoc}, citeulike-linkout-2 = {http://view.ncbi.nlm.nih.gov/pubmed/19136553}, citeulike-linkout-3 = {http://www.hubmed.org/display.cgi?uids=19136553}, day = {15}, doi = {10.1093/bioinformatics/btn658}, journal = {Bioinformatics}, keywords = {module\_detection, network, regulation}, month = {February}, number = {4}, pages = {490--496}, posted-at = {2009-04-15 13:57:54}, priority = {3}, title = {Module networks revisited: computational assessment and prioritization of model predictions}, url = {http://dx.doi.org/10.1093/bioinformatics/btn658}, volume = {25}, year = {2009} }

TY - JOUR ID - citeulike:4044418 L3 - citeulike-article-id:4044418 N2 - Motivation: The solution of high-dimensional inference and prediction problems in computational biology is almost always a compromise between mathematical theory and practical constraints, such as limited computational resources. As time progresses, computational power increases but well-established inference methods often remain locked in their initial suboptimal solution. Results: We revisit the approach of Segal et al. to infer regulatory modules and their condition-specific regulators from gene expression data. In contrast to their direct optimization-based solution, we use a more representative centroid-like solution extracted from an ensemble of possible statistical models to explain the data. The ensemble method automatically selects a subset of most informative genes and builds a quantitatively better model for them. Genes which cluster together in the majority of models produce functionally more coherent modules. Regulators which are consistently assigned to a module are more often supported by literature, but a single model always contains many regulator assignments not supported by the ensemble. Reliably detecting condition-specific or combinatorial regulation is particularly hard in a single optimum but can be achieved using ensemble averaging. Availability: All software developed for this study is available from http://bioinformatics.psb.ugent.be/software. Contact: tom.michoel@psb.ugent.be Supplementary information: Supplementary data and figures are available from http://bioinformatics.psb.ugent.be/supplementary_data/anjos/module_nets_yeast/. 10.1093/bioinformatics/btn658 IS - 4 JF - Bioinformatics EP - 496 TI - Module networks revisited: computational assessment and prioritization of model predictions VL - 25 SP - 490 KW - module_detection KW - network KW - regulation AU - Joshi, Anagha AU - De Smet, Riet AU - Marchal, Kathleen AU - Van de Peer, Yves AU - Michoel, Tom PY - 2009/02/15/ UR - http://dx.doi.org/10.1093/bioinformatics/btn658 ER -