{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,14]],"date-time":"2026-02-14T01:58:10Z","timestamp":1771034290563,"version":"3.50.1"},"reference-count":24,"publisher":"Oxford University Press (OUP)","issue":"22","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2009,11,15]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Motivation: Regulation of gene expression is fundamental to the operation of a cell. Revealing the structure and dynamics of a gene regulatory network (GRN) is of great interest and represents a considerably challenging computational problem. The GRN estimation problem is complicated by the fact that the number of gene expression measurements is typically extremely small when compared with the dimension of the biological system. Further, because the gene regulation process is intrinsically complex, commonly used parametric models can provide too simple description of the underlying phenomena and, thus, can be unreliable. In this article, we propose a novel methodology for the inference of GRNs from time-series and steady-state gene expression measurements. The presented framework is based on the use of Bayesian analysis with ordinary differential equations (ODEs) and non-parametric Gaussian process modeling for the transcriptional-level regulation.<\/jats:p>\n               <jats:p>Results: The performance of the proposed structure inference method is evaluated using a recently published in vivo dataset. By comparing the obtained results with those of existing ODE- and Bayesian-based inference methods we demonstrate that the proposed method provides more accurate network structure learning. The predictive capabilities of the method are examined by splitting the dataset into a training set and a test set and by predicting the test set based on the training set.<\/jats:p>\n               <jats:p>Availability: A MATLAB implementation of the method will be available from http:\/\/www.cs.tut.fi\/~aijo2\/gp upon publication<\/jats:p>\n               <jats:p>Contact: \u00a0harri.lahdesmaki@tut.fi<\/jats:p>\n               <jats:p>Supplementary information: \u00a0Supplementary data are available at Bioinformatics online.<\/jats:p>","DOI":"10.1093\/bioinformatics\/btp511","type":"journal-article","created":{"date-parts":[[2009,8,26]],"date-time":"2009-08-26T03:34:49Z","timestamp":1251257689000},"page":"2937-2944","source":"Crossref","is-referenced-by-count":70,"title":["Learning gene regulatory networks from gene expression measurements using non-parametric molecular kinetics"],"prefix":"10.1093","volume":"25","author":[{"given":"Tarmo","family":"\u00c4ij\u00f6","sequence":"first","affiliation":[{"name":"1 Department of Signal Processing, Tampere University of Technology, Tampere and 2Department of Information and Computer Science, Helsinki University of Technology, Helsinki, Finland"}]},{"given":"Harri","family":"L\u00e4hdesm\u00e4ki","sequence":"additional","affiliation":[{"name":"1 Department of Signal Processing, Tampere University of Technology, Tampere and 2Department of Information and Computer Science, Helsinki University of Technology, Helsinki, Finland"},{"name":"1 Department of Signal Processing, Tampere University of Technology, Tampere and 2Department of Information and Computer Science, Helsinki University of Technology, Helsinki, Finland"}]}],"member":"286","published-online":{"date-parts":[[2009,8,25]]},"reference":[{"key":"2023013112155666600_B1","doi-asserted-by":"crossref","first-page":"815","DOI":"10.1093\/bioinformatics\/btl003","article-title":"Inference of gene regulatory networks and compound mode of action from time course gene expression profiles","volume":"22","author":"Bansal","year":"2006","journal-title":"Bioinformatics"},{"key":"2023013112155666600_B2","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1038\/msb4100120","article-title":"How to infer gene networks from expression profiles","volume":"3","author":"Bansal","year":"2007","journal-title":"Mol. Syst. Biol."},{"key":"2023013112155666600_B3","doi-asserted-by":"crossref","first-page":"R25","DOI":"10.1186\/gb-2006-7-3-r25","article-title":"Ranked prediction of p53 targets using hidden variable dynamic modeling","volume":"7","author":"Barenco","year":"2006","journal-title":"Genome Biol"},{"key":"2023013112155666600_B4","doi-asserted-by":"crossref","first-page":"R36","DOI":"10.1186\/gb-2006-7-5-r36","article-title":"The Inferelator: an algorithm for learning parsimonious regulatory networks from systems-biology data sets de novo","volume":"7","author":"Bonneau","year":"2006","journal-title":"Genome Biol."},{"key":"2023013112155666600_B5","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1016\/j.cell.2009.01.055","article-title":"A yeast synthetic network for in vivo assessment of reverse-engineering and modeling approaches","volume":"137","author":"Cantone","year":"2009","journal-title":"Cell"},{"key":"2023013112155666600_B6","doi-asserted-by":"crossref","first-page":"1619","DOI":"10.1093\/bioinformatics\/btn246","article-title":"Estimating dynamic models for gene regulation networks","volume":"24","author":"Cao","year":"2008","journal-title":"Bioinformatics"},{"key":"2023013112155666600_B7","first-page":"41","article-title":"Linear modeling of mRNA expression levels during CNS development and injury","volume-title":"Proceedings of Pacific Symposium on Biocomputing (PSB 99).","author":"D'haeseleer","year":"1999"},{"key":"2023013112155666600_B8","doi-asserted-by":"crossref","first-page":"i70","DOI":"10.1093\/bioinformatics\/btn278","article-title":"Gaussian process modelling of latent chemical species: applications to inferring transcription factor activities","volume":"24","author":"Gao","year":"2008","journal-title":"Bioinformatics"},{"key":"2023013112155666600_B9","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1126\/science.1081900","article-title":"Inferring genetic networks and identifying compound mode of action via expression profiling","volume":"301","author":"Gardner","year":"2003","journal-title":"Science"},{"key":"2023013112155666600_B10","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1038\/nrm1588","article-title":"Thinking quantitatively about transcriptional regulation","volume":"6","author":"Greive","year":"2005","journal-title":"Nat. Rev. Mol. Cell Biol."},{"key":"2023013112155666600_B11","first-page":"175","article-title":"Estimation of genetic networks and functional structures between genes by using Bayesian networks and nonparametric regression","volume":"7","author":"Imoto","year":"2002","journal-title":"Pac. Symp. Biocomput."},{"key":"2023013112155666600_B12","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1111\/j.1749-6632.2008.03945.x","article-title":"Combining multiple results of a reverse-engineering algorithm: application to the DREAM five-gene network challenge","volume":"1158","author":"Marbach","year":"2009","journal-title":"Ann. N. Y. Acad. Sci."},{"key":"2023013112155666600_B13","doi-asserted-by":"crossref","first-page":"234","DOI":"10.1111\/j.1749-6632.2008.03944.x","article-title":"Replaying the evolutionary tape: biomimetic reverse engineering of gene networks","volume":"1158","author":"Marbach","year":"2009","journal-title":"Ann. N. Y. Acad. Sci."},{"issue":"Suppl. 1","key":"2023013112155666600_B14","doi-asserted-by":"crossref","first-page":"S7","DOI":"10.1186\/1471-2105-7-S1-S7","article-title":"ARACNE: an algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context","volume":"7","author":"Margolin","year":"2006","journal-title":"BMC Bioinformatics"},{"issue":"Suppl. 6","key":"2023013112155666600_B15","doi-asserted-by":"crossref","first-page":"S5","DOI":"10.1186\/1471-2105-8-S6-S5","article-title":"Inferring cellular networks\u2013a review","volume":"8","author":"Markowetz","year":"2007","journal-title":"BMC Bioinformatics"},{"issue":"Suppl. 1","key":"2023013112155666600_B16","doi-asserted-by":"crossref","first-page":"i248","DOI":"10.1093\/bioinformatics\/bth941","article-title":"Inferring quantitative models of regulatory networks from expression data","volume":"20","author":"Nachman","year":"2004","journal-title":"Bioinformatics"},{"issue":"Suppl. 2","key":"2023013112155666600_B17","doi-asserted-by":"crossref","first-page":"ii138","DOI":"10.1093\/bioinformatics\/btg1071","article-title":"Gene networks inference using dynamic Bayesian networks","volume":"19","author":"Perrin","year":"2003","journal-title":"Bioinformatics"},{"key":"2023013112155666600_B18","doi-asserted-by":"crossref","DOI":"10.7551\/mitpress\/3206.001.0001","volume-title":"Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning).","author":"Rasmussen","year":"2005"},{"key":"2023013112155666600_B19","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1093\/bioinformatics\/18.2.261","article-title":"Probabilistic Boolean networks: a rule-based uncertainty model for gene regulatory networks","volume":"18","author":"Shmulevich","year":"2002","journal-title":"Bioinformatics"},{"key":"2023013112155666600_B20","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1196\/annals.1407.021","article-title":"Dialogue on reverse-engineering assessment and methods: the DREAM of high-throughput pathway inference","volume":"1115","author":"Stolovitzky","year":"2007","journal-title":"Ann. N. Y. Acad. Sci."},{"key":"2023013112155666600_B21","doi-asserted-by":"crossref","first-page":"3056","DOI":"10.1093\/bioinformatics\/btm465","article-title":"Inferring transcriptional regulatory networks from high-throughput data","volume":"23","author":"Wang","year":"2007","journal-title":"Bioinformatics"},{"key":"2023013112155666600_B22","doi-asserted-by":"crossref","DOI":"10.1201\/9781420010664","volume-title":"Stochastic Modelling for Systems Biology","author":"Wilkinson","year":"2006","edition":"1st"},{"key":"2023013112155666600_B23","doi-asserted-by":"crossref","first-page":"3594","DOI":"10.1093\/bioinformatics\/bth448","article-title":"Advances to Bayesian network inference for generating causal networks from observational biological data","volume":"20","author":"Yu","year":"2004","journal-title":"Bioinformatics"},{"key":"2023013112155666600_B24","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1093\/bioinformatics\/bth463","article-title":"A new dynamic Bayesian network (DBN) approach for identifying gene regulatory networks from time course microarray data","volume":"21","author":"Zou","year":"2005","journal-title":"Bioinformatics"}],"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/25\/22\/2937\/48998228\/bioinformatics_25_22_2937.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/25\/22\/2937\/48998228\/bioinformatics_25_22_2937.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,31]],"date-time":"2023-01-31T21:55:22Z","timestamp":1675202122000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article\/25\/22\/2937\/179576"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2009,8,25]]},"references-count":24,"journal-issue":{"issue":"22","published-print":{"date-parts":[[2009,11,15]]}},"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/btp511","relation":{},"ISSN":["1367-4811","1367-4803"],"issn-type":[{"value":"1367-4811","type":"electronic"},{"value":"1367-4803","type":"print"}],"subject":[],"published-other":{"date-parts":[[2009,11,15]]},"published":{"date-parts":[[2009,8,25]]}}}