{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T09:48:16Z","timestamp":1775036896772,"version":"3.50.1"},"reference-count":33,"publisher":"Oxford University Press (OUP)","issue":"12","license":[{"start":{"date-parts":[[2018,11,5]],"date-time":"2018-11-05T00:00:00Z","timestamp":1541376000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000921","name":"European Cooperation in Science and Technology","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100000921","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000921","name":"COST","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100000921","id-type":"DOI","asserted-by":"publisher"}]},{"name":"European Cooperation for Statistics of Network Data Science"},{"name":"COSTNET"},{"name":"GlioPATH","award":["01ZX1402B"],"award-info":[{"award-number":["01ZX1402B"]}]},{"name":"MAPTor-NET","award":["031A426B"],"award-info":[{"award-number":["031A426B"]}]},{"DOI":"10.13039\/501100001659","name":"German Research Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001659","id-type":"DOI","asserted-by":"publisher"}]},{"name":"MESI-STRAT project"},{"name":"European Union\u2019s Horizon 2020 research","award":["754688"],"award-info":[{"award-number":["754688"]}]},{"name":"Rosalind-Franklin-Fellowship of the University of Groningen"},{"name":"Research Award 2017 of the German Tuberous Sclerosis Foundation"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,6,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Non-homogeneous dynamic Bayesian networks (NH-DBNs) are a popular modelling tool for learning cellular networks from time series data. In systems biology, time series are often measured under different experimental conditions, and not rarely only some network interaction parameters depend on the condition while the other parameters stay constant across conditions. For this situation, we propose a new partially NH-DBN, based on Bayesian hierarchical regression models with partitioned design matrices. With regard to our main application to semi-quantitative (immunoblot) timecourse data from mammalian target of rapamycin complex 1 (mTORC1) signalling, we also propose a Gaussian process-based method to solve the problem of non-equidistant time series measurements.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>On synthetic network data and on yeast gene expression data the new model leads to improved network reconstruction accuracies. We then use the new model to reconstruct the topologies of the circadian clock network in Arabidopsis thaliana and the mTORC1 signalling pathway. The inferred network topologies show features that are consistent with the biological literature.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>All datasets have been made available with earlier publications. Our Matlab code is available upon request.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Supplementary information<\/jats:title>\n                  <jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/bty917","type":"journal-article","created":{"date-parts":[[2018,11,2]],"date-time":"2018-11-02T21:14:38Z","timestamp":1541193278000},"page":"2108-2117","source":"Crossref","is-referenced-by-count":12,"title":["Partially non-homogeneous dynamic Bayesian networks based on Bayesian regression models with partitioned design matrices"],"prefix":"10.1093","volume":"35","author":[{"given":"Mahdi","family":"Shafiee Kamalabad","sequence":"first","affiliation":[{"name":"Department of Mathematics, Bernoulli Institute, Faculty of Science and Engineering, University of Groningen, AG Groningen, The Netherlands"}]},{"given":"Alexander Martin","family":"Heberle","sequence":"additional","affiliation":[{"name":"Laboratory of Pediatrics, Section Systems Medicine of Metabolism and Signaling, University of Groningen, University Medical Center Groningen, AV Groningen, The Netherlands"}]},{"given":"Kathrin","family":"Thedieck","sequence":"additional","affiliation":[{"name":"Laboratory of Pediatrics, Section Systems Medicine of Metabolism and Signaling, University of Groningen, University Medical Center Groningen, AV Groningen, The Netherlands"},{"name":"Department for Neuroscience, School of Medicine and Health Sciences, Carl von Ossietzky University Oldenburg, Oldenburg, Germany"}]},{"given":"Marco","family":"Grzegorczyk","sequence":"additional","affiliation":[{"name":"Department of Mathematics, Bernoulli Institute, Faculty of Science and Engineering, University of Groningen, AG Groningen, The Netherlands"}]}],"member":"286","published-online":{"date-parts":[[2018,11,5]]},"reference":[{"key":"2023012713234016600_bty917-B1","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1515\/sagmb-2013-0051","article-title":"Statistical inference of regulatory networks for circadian regulation","volume":"13","author":"Aderhold","year":"2014","journal-title":"Stat. 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