Dynamic Bayesian network and nonparametric regression for nonlinear modeling of gene networks from time series gene expression data Export

@article{Kim2004, abstract = {We propose a dynamic Bayesian network and nonparametric regression model for constructing a gene network from time series microarray gene expression data. The proposed method can overcome a shortcoming of the Bayesian network model in the sense of the construction of cyclic regulations. The proposed method can analyze the microarray data as a continuous data and can capture even nonlinear relations among genes. It can be expected that this model will give a deeper insight into complicated biological systems. We also derive a new criterion for evaluating an estimated network from Bayes approach. We conduct Monte Carlo experiments to examine the effectiviness of the proposed method. We also demonstrate the proposed method through the analysis of the Saccharomyces cerevisiae gene expression data.}, author = {Kim, Sunyong and Imoto, Seiya and Miyano, Satoru}, citeulike-article-id = {441786}, citeulike-linkout-0 = {http://dx.doi.org/10.1016/j.biosystems.2004.03.004}, citeulike-linkout-1 = {http://linkinghub.elsevier.com/retrieve/pii/S0303264704000383}, citeulike-linkout-2 = {http://view.ncbi.nlm.nih.gov/pubmed/15245804}, citeulike-linkout-3 = {http://www.hubmed.org/display.cgi?uids=15245804}, citeulike-linkout-4 = {http://www.sciencedirect.com/science/article/B6T2K-4CT49S5-9/2/e11bead489b32d882ae3c369f20e4ed8}, doi = {10.1016/j.biosystems.2004.03.004}, issn = {03032647}, journal = {Biosystems}, keywords = {a\_exam, dynamic, inference}, month = {July}, number = {1-3}, pages = {57--65}, posted-at = {2005-12-19 14:27:50}, priority = {2}, title = {Dynamic Bayesian network and nonparametric regression for nonlinear modeling of gene networks from time series gene expression data}, url = {http://dx.doi.org/10.1016/j.biosystems.2004.03.004}, volume = {75}, year = {2004} }

TY - JOUR ID - Kim2004 L3 - citeulike-article-id:441786 N2 - We propose a dynamic Bayesian network and nonparametric regression model for constructing a gene network from time series microarray gene expression data. The proposed method can overcome a shortcoming of the Bayesian network model in the sense of the construction of cyclic regulations. The proposed method can analyze the microarray data as a continuous data and can capture even nonlinear relations among genes. It can be expected that this model will give a deeper insight into complicated biological systems. We also derive a new criterion for evaluating an estimated network from Bayes approach. We conduct Monte Carlo experiments to examine the effectiviness of the proposed method. We also demonstrate the proposed method through the analysis of the Saccharomyces cerevisiae gene expression data. IS - 1-3 JF - Biosystems SN - 03032647 EP - 65 TI - Dynamic Bayesian network and nonparametric regression for nonlinear modeling of gene networks from time series gene expression data VL - 75 SP - 57 KW - a_exam KW - dynamic KW - inference AU - Kim, Sunyong AU - Imoto, Seiya AU - Miyano, Satoru PY - 2004/07// UR - http://dx.doi.org/10.1016/j.biosystems.2004.03.004 ER -