{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,6]],"date-time":"2026-06-06T17:56:48Z","timestamp":1780768608647,"version":"3.54.1"},"reference-count":8,"publisher":"Oxford University Press (OUP)","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2005,1,1]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Motivation: Signaling pathways are dynamic events that take place over a given period of time. In order to identify these pathways, expression data over time are required. Dynamic Bayesian network (DBN) is an important approach for predicting the gene regulatory networks from time course expression data. However, two fundamental problems greatly reduce the effectiveness of current DBN methods. The first problem is the relatively low accuracy of prediction, and the second is the excessive computational time.<\/jats:p><jats:p>Results: In this paper, we present a DBN-based approach with increased accuracy and reduced computational time compared with existing DBN methods. Unlike previous methods, our approach limits potential regulators to those genes with either earlier or simultaneous expression changes (up- or down-regulation) in relation to their target genes. This allows us to limit the number of potential regulators and consequently reduce the search space. Furthermore, we use the time difference between the initial change in the expression of a given regulator gene and its potential target gene to estimate the transcriptional time lag between these two genes. This method of time lag estimation increases the accuracy of predicting gene regulatory networks. Our approach is evaluated using time-series expression data measured during the yeast cell cycle. The results demonstrate that this approach can predict regulatory networks with significantly improved accuracy and reduced computational time compared with existing DBN approaches.<\/jats:p><jats:p>Availability: The programs described in this paper can be obtained from the corresponding author upon request.<\/jats:p><jats:p>Contact: \u00a0sconzen@medicine.bsd.uchicago.edu<\/jats:p>","DOI":"10.1093\/bioinformatics\/bth463","type":"journal-article","created":{"date-parts":[[2004,8,13]],"date-time":"2004-08-13T00:15:36Z","timestamp":1092356136000},"page":"71-79","source":"Crossref","is-referenced-by-count":460,"title":["A new dynamic Bayesian network (DBN) approach for identifying gene regulatory networks from time course microarray data"],"prefix":"10.1093","volume":"21","author":[{"given":"Min","family":"Zou","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Suzanne D.","family":"Conzen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2004,8,12]]},"reference":[{"key":"2023013107193003900_B1","doi-asserted-by":"crossref","unstructured":"Chou, R.J., Campbell, M.J., Winzeler, E.A., Steinmetz,L., Conway,A., Wodicka, L., Wolfsberg, T.G., Gabrielian, A.E., Landsman, D., Lockhart, D.J., Davis, R.W. 1998A genome-wide transcriptional analysis of the mitotic cell cycle. Mol. Cell265\u201373","DOI":"10.1016\/S1097-2765(00)80114-8"},{"key":"2023013107193003900_B2","doi-asserted-by":"crossref","unstructured":"Horak, C.E., Luscombe, N.M., Qian, J., Bertone, P., Spiccirrillo, S., Gerstein, M., Snyder, M. 2002Complex transcriptional circuitry at the G1\/S transition in Saccharomyces cerevisiae . Genes Dev.163017\u20133033","DOI":"10.1101\/gad.1039602"},{"key":"2023013107193003900_B3","unstructured":"Imoto, S., Goto, T., Miyano, S. 2002Estimation of genetic networks and functional structures between genes by using Bayesian network and nonparametric regression. Pac. Symp. Biocomput.7175\u2013186"},{"key":"2023013107193003900_B4","unstructured":"Kim, S.Y., Imoto, S., Miyano, S. 2003Inferring gene networks from time series microarray data using dynamic Bayesian networks. Brief Bioinform.4228\u2013235"},{"key":"2023013107193003900_B5","unstructured":"Murphy, K. and Mian, S. 1999Modeling gene expression data using dynamic Bayesian networks. Technical Report , Berkeley, CA Computer Science Division, University of California"},{"key":"2023013107193003900_B6","doi-asserted-by":"crossref","unstructured":"Perrin, B.E., Ralaivola, L., Mazurie, A., Bottani, S., Mallet, J., D'Alche-Buc, F. 2003Gene networks inference using dynamic Bayesian networks. Bioinformatics19(Suppl.2),, pp. II138\u2013II148","DOI":"10.1093\/bioinformatics\/btg1071"},{"key":"2023013107193003900_B7","doi-asserted-by":"crossref","unstructured":"Simon, I., Barnett, J., Hannett, N., Harbison, C.T., Rinaldi, N.J., Volkert, T.L., Wyrick, J.J., Zeitlinger, J., Gifford, D.K., Jaakkola, T.S., Young, R.A. 2001Serial regulation of transcriptional regulators in the yeast cell cycle. Cell106697\u2013708","DOI":"10.1016\/S0092-8674(01)00494-9"},{"key":"2023013107193003900_B8","doi-asserted-by":"crossref","unstructured":"Yu, H., Luscombe, N.M., Qian, J., Gerstein, M. 2003Genomic analysis of gene expression relationships in transcriptional regulatory networks. Trends Genet.19422\u2013427","DOI":"10.1016\/S0168-9525(03)00175-6"}],"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/21\/1\/71\/48961871\/bioinformatics_21_1_71.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/21\/1\/71\/48961871\/bioinformatics_21_1_71.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,4,29]],"date-time":"2023-04-29T05:16:27Z","timestamp":1682745387000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article\/21\/1\/71\/212416"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2004,8,12]]},"references-count":8,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2005,1,1]]}},"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/bth463","relation":{},"ISSN":["1367-4811","1367-4803"],"issn-type":[{"value":"1367-4811","type":"electronic"},{"value":"1367-4803","type":"print"}],"subject":[],"published-other":{"date-parts":[[2005,1,1]]},"published":{"date-parts":[[2004,8,12]]}}}