A gene network inference method from continuous-value gene expression data of wild-type and mutants.

@article{citeulike:307875, abstract = {In this paper we introduce a new inference method of a gene regulatory network from steady-state gene expression data. Our method determines a regulatory structure consistent with an observed set of steady-state expression profiles, each generated from wild-type and single deletion mutant of the target network. Our method derives the regulatory relationships in the network using a graph theoretic approach. The advantage of our method is to be able to deal with continuous values of steady-state data, while most of the methods proposed in past use a Boolean network model with binary data. Performance of our method is evaluated on simulated networks with varying the size of networks, indegree of each gene, and the data characteristics (continuous-value/binary), and is compared with that of predictor method proposed by Ideker et al. As a result, we show the superiority of using continuous values to binary values, and the performance of our method is much better than that of the predictor method.}, address = {Kitano Symbiotic Systems Project, ERATO, JST., M31 6A, 6-31-15 Jingumae, Shibuya-ku, Tokyo 150-0001, Japan. kyoda@symbio.jst.go.jp}, author = {Kyoda, K. M. and Morohashi, M. and Onami, S. and Kitano, H. }, citeulike-article-id = {307875}, issn = {0919-9454}, journal = {Genome Inform Ser Workshop Genome Inform}, keywords = {genetic\_regulatory\_network}, pages = {196--204}, posted-at = {2007-10-05 10:16:46}, priority = {3}, title = {A gene network inference method from continuous-value gene expression data of wild-type and mutants.}, url = {http://view.ncbi.nlm.nih.gov/pubmed/11700600}, volume = {11}, year = {2000} }

TY - JOUR ID - citeulike:307875 L3 - citeulike-article-id:307875 TI - A gene network inference method from continuous-value gene expression data of wild-type and mutants. JF - Genome Inform Ser Workshop Genome Inform VL - 11 SP - 196 EP - 204 AD - Kitano Symbiotic Systems Project, ERATO, JST., M31 6A, 6-31-15 Jingumae, Shibuya-ku, Tokyo 150-0001, Japan. kyoda@symbio.jst.go.jp SN - 0919-9454 N2 - In this paper we introduce a new inference method of a gene regulatory network from steady-state gene expression data. Our method determines a regulatory structure consistent with an observed set of steady-state expression profiles, each generated from wild-type and single deletion mutant of the target network. Our method derives the regulatory relationships in the network using a graph theoretic approach. The advantage of our method is to be able to deal with continuous values of steady-state data, while most of the methods proposed in past use a Boolean network model with binary data. Performance of our method is evaluated on simulated networks with varying the size of networks, indegree of each gene, and the data characteristics (continuous-value/binary), and is compared with that of predictor method proposed by Ideker et al. As a result, we show the superiority of using continuous values to binary values, and the performance of our method is much better than that of the predictor method. KW - genetic_regulatory_network AU - Kyoda, KM AU - Morohashi, M AU - Onami, S AU - Kitano, H PY - 2000/// UR - http://view.ncbi.nlm.nih.gov/pubmed/11700600 ER -