{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,14]],"date-time":"2025-06-14T04:07:03Z","timestamp":1749874023657,"version":"3.41.0"},"reference-count":53,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2016,12,15]],"date-time":"2016-12-15T00:00:00Z","timestamp":1481760000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2016,12,15]],"date-time":"2016-12-15T00:00:00Z","timestamp":1481760000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Bioinformatics"],"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Background<\/jats:title><jats:p>Biological systems and processes are highly dynamic. To gain insights into their functioning time-resolved measurements are necessary. Time-resolved gene expression data captures temporal behaviour of the genes genome-wide under various biological conditions: in response to stimuli, during cell cycle, differentiation or developmental programs. Dissecting dynamic gene expression patterns from this data may shed light on the functioning of the gene regulatory system. The present approach facilitates this discovery. The fundamental idea behind it is the following: there are change-points (switches) in the gene behaviour separating intervals of increasing and decreasing activity, whereas the intervals may have different durations. Elucidating the switch-points is important for the identification of biologically meanigfull features and patterns of the gene dynamics.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>We developed a statistical method, called SwitchFinder, for the analysis of time-series data, in particular gene expression data, based on a change-point model. Fitting the model to the gene expression time-courses indicates switch-points between increasing and decreasing activities of each gene. Two types of the model - based on linear and on generalized logistic function - were used to capture the data between the switch-points. Model inference was facilitated with the Bayesian methodology using Markov chain Monte Carlo (MCMC) technique Gibbs sampling. Further on, we introduced features of the switch-points:<jats:italic>growth<\/jats:italic>,<jats:italic>decay<\/jats:italic>,<jats:italic>spike<\/jats:italic>and<jats:italic>cleft<\/jats:italic>, which reflect important dynamic aspects. With this, the gene expression profiles are represented in a qualitative manner - as sets of the dynamic features at their onset-times. We developed a Web application of the approach, enabling to put queries to the gene expression time-courses and to deduce groups of genes with common dynamic patterns.<\/jats:p><jats:p>SwitchFinder was applied to our original data - the gene expression time-series measured in neuroblastoma cell line upon treatment with all-<jats:italic>trans<\/jats:italic>retinoic acid (ATRA). The analysis revealed eight patterns of the gene expression responses to ATRA, indicating the induction of the BMP, WNT, Notch, FGF and NTRK-receptor signaling pathways involved in cell differentiation, as well as the repression of the cell-cycle related genes.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>SwitchFinder is a novel approach to the analysis of biological time-series data, supporting inference and interactive exploration of its inherent dynamic patterns, hence facilitating biological discovery process. SwitchFinder is freely available at https:\/\/newbioinformatics.eu\/switchfinder.<\/jats:p><\/jats:sec>","DOI":"10.1186\/s12859-016-1391-0","type":"journal-article","created":{"date-parts":[[2016,12,15]],"date-time":"2016-12-15T13:09:39Z","timestamp":1481807379000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["SwitchFinder \u2013 a novel method and query facility for discovering dynamic gene expression patterns"],"prefix":"10.1186","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8802-5602","authenticated-orcid":false,"given":"Svetlana","family":"Bulashevska","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Colin","family":"Priest","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Daniel","family":"Speicher","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"J\u00f6rg","family":"Zimmermann","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Frank","family":"Westermann","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Armin B.","family":"Cremers","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2016,12,15]]},"reference":[{"issue":"8","key":"1391_CR1","doi-asserted-by":"publisher","first-page":"552","DOI":"10.1038\/nrg3244","volume":"13","author":"Z Bar-Joseph","year":"2012","unstructured":"Bar-Joseph Z, Gitter A, Simon I. Studying and modelling dynamic biological processes using time-series gene expression data. Nat Rev Genet. 2012; 13(8):552\u201364.","journal-title":"Nat Rev Genet"},{"issue":"suppl 1","key":"1391_CR2","doi-asserted-by":"publisher","first-page":"255","DOI":"10.1093\/bioinformatics\/btg1036","volume":"19","author":"A Schliep","year":"2003","unstructured":"Schliep A, Sch\u00f6nhuth A, Steinhoff C. Using hidden markov models to analyze gene expression time course data. Bioinformatics. 2003; 19(suppl 1):255\u201363.","journal-title":"Bioinformatics"},{"issue":"1","key":"1391_CR3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/1756-0381-4-9","volume":"4","author":"R Schilling","year":"2011","unstructured":"Schilling R, Costa IG, Schliep A. pgql: A probabilistic graphical query language for gene expression time courses. BioData Min. 2011; 4(1):1.","journal-title":"BioData Min"},{"issue":"7","key":"1391_CR4","doi-asserted-by":"publisher","first-page":"946","DOI":"10.1093\/bioinformatics\/btr037","volume":"27","author":"C Hafemeister","year":"2011","unstructured":"Hafemeister C, Costa IG, Sch\u00f6nhuth A, Schliep A. Classifying short gene expression time-courses with bayesian estimation of piecewise constant functions. Bioinformatics. 2011; 27(7):946\u201352.","journal-title":"Bioinformatics"},{"issue":"14","key":"1391_CR5","doi-asserted-by":"publisher","first-page":"9121","DOI":"10.1073\/pnas.132656399","volume":"99","author":"MF Ramoni","year":"2002","unstructured":"Ramoni MF, Sebastiani P, Kohane IS. Cluster analysis of gene expression dynamics. Proc Natl Acad Sci. 2002; 99(14):9121\u2013126.","journal-title":"Proc Natl Acad Sci"},{"issue":"2","key":"1391_CR6","doi-asserted-by":"publisher","first-page":"279","DOI":"10.1089\/cmb.2008.13TT","volume":"16","author":"G Chechik","year":"2009","unstructured":"Chechik G, Koller D. Timing of gene expression responses to environmental changes. J Comput Biol. 2009; 16(2):279\u201390.","journal-title":"J Comput Biol"},{"issue":"13","key":"1391_CR7","doi-asserted-by":"publisher","first-page":"392","DOI":"10.1093\/bioinformatics\/btr250","volume":"27","author":"J Sivriver","year":"2011","unstructured":"Sivriver J, Habib N, Friedman N. An integrative clustering and modeling algorithm for dynamical gene expression data. Bioinformatics. 2011; 27(13):392\u2013400.","journal-title":"Bioinformatics"},{"issue":"1","key":"1391_CR8","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1016\/0304-4076(90)90093-9","volume":"45","author":"JD Hamilton","year":"1990","unstructured":"Hamilton JD. Analysis of time series subject to changes in regime. J Econ. 1990; 45(1):39\u201370.","journal-title":"J Econ"},{"key":"1391_CR9","doi-asserted-by":"crossref","unstructured":"Hamilton JD. Estimation, inference and forecasting of time series subject to changes in regime. Handbook of statistics, Vol. 11, North-Holland. 1993;:231\u201360.","DOI":"10.1016\/S0169-7161(05)80044-6"},{"key":"1391_CR10","doi-asserted-by":"crossref","unstructured":"Carlin BP, Gelfand AE, Smith AF. Hierarchical bayesian analysis of changepoint problems. Applied statistics. 1992;:389\u2013405.","DOI":"10.2307\/2347570"},{"issue":"4","key":"1391_CR11","doi-asserted-by":"publisher","first-page":"190","DOI":"10.1890\/070179","volume":"7","author":"DL Sonderegger","year":"2008","unstructured":"Sonderegger DL, Wang H, Clements WH, Noon BR. Using sizer to detect thresholds in ecological data. Front Ecol Environ. 2008; 7(4):190\u20135.","journal-title":"Front Ecol Environ"},{"issue":"3","key":"1391_CR12","doi-asserted-by":"crossref","first-page":"374","DOI":"10.1080\/07350015.2000.10524878","volume":"18","author":"J Wang","year":"2000","unstructured":"Wang J, Zivot E. A bayesian time series model of multiple structural changes in level, trend, and variance. J Bus Econ Stat. 2000; 18(3):374\u201386.","journal-title":"J Bus Econ Stat"},{"key":"1391_CR13","doi-asserted-by":"crossref","unstructured":"Koop GM, Potter S. Forecasting and estimating multiple change-point models with an unknown number of change points, Federal Reserve Bank of New York, Staff Reports. 2004.","DOI":"10.2139\/ssrn.628561"},{"issue":"3","key":"1391_CR14","doi-asserted-by":"publisher","first-page":"395","DOI":"10.1007\/s11222-010-9177-0","volume":"21","author":"CW Chen","year":"2011","unstructured":"Chen CW, Chan JS, Gerlach R, Hsieh WY. A comparison of estimators for regression models with change points. Stat Comput. 2011; 21(3):395\u2013414.","journal-title":"Stat Comput"},{"issue":"3","key":"1391_CR15","doi-asserted-by":"publisher","first-page":"181","DOI":"10.1016\/0167-7152(88)90118-6","volume":"6","author":"YC Yao","year":"1988","unstructured":"Yao YC. Estimating the number of change-points via schwarz\u2019criterion. Stat Probab Lett. 1988; 6(3):181\u20139.","journal-title":"Stat Probab Lett"},{"issue":"19","key":"1391_CR16","doi-asserted-by":"publisher","first-page":"3055","DOI":"10.1002\/sim.1545","volume":"22","author":"VM Muggeo","year":"2003","unstructured":"Muggeo VM. Estimating regression models with unknown break-points. Stat Med. 2003; 22(19):3055\u2013071.","journal-title":"Stat Med"},{"key":"1391_CR17","unstructured":"Muggeo V. Segmented: An R package to fit regression models with broken-line relationships. R News, 8\/1. 2008;:20\u201325."},{"issue":"1","key":"1391_CR18","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1002\/jae.659","volume":"18","author":"J Bai","year":"2003","unstructured":"Bai J, Perron P. Computation and analysis of multiple structural change models. J Appl Econ. 2003; 18(1):1\u201322.","journal-title":"J Appl Econ"},{"issue":"2","key":"1391_CR19","first-page":"1","volume":"7","author":"C Kleiber","year":"2002","unstructured":"Kleiber C, Hornik K, Leisch F, Zeileis A. strucchange: An r package for testing for structural change in linear regression models. J Stat Softw. 2002; 7(2):1\u201338.","journal-title":"J Stat Softw"},{"issue":"1","key":"1391_CR20","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1016\/S0167-9473(03)00030-6","volume":"44","author":"A Zeileis","year":"2003","unstructured":"Zeileis A, Kleiber C, Kr\u00e4mer W, Hornik K. Testing and dating of structural changes in practice. Comput Stat Data Anal. 2003; 44(1):109\u201323.","journal-title":"Comput Stat Data Anal"},{"issue":"3","key":"1391_CR21","doi-asserted-by":"publisher","first-page":"999","DOI":"10.1214\/aoms\/1177700517","volume":"35","author":"H Chernoff","year":"1964","unstructured":"Chernoff H, Zacks S. Estimating the current mean of a normal distribution which is subjected to changes in time. Ann Math Stat. 1964; 35(3):999\u20131018.","journal-title":"Ann Math Stat"},{"key":"1391_CR22","doi-asserted-by":"crossref","unstructured":"Lai TL, Xing H. A simple bayesian approach to multiple change-points. Statistica Sinica. 2011;:539\u2013569.","DOI":"10.5705\/ss.2011.025a"},{"key":"1391_CR23","doi-asserted-by":"crossref","unstructured":"Stephens D. Bayesian retrospective multiple-changepoint identification. Applied Statistics. 1994;:159\u2013178.","DOI":"10.2307\/2986119"},{"issue":"421","key":"1391_CR24","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1080\/01621459.1993.10594323","volume":"88","author":"D Barry","year":"1993","unstructured":"Barry D, Hartigan JA. A bayesian analysis for change point problems. J Am Stat Assoc. 1993; 88(421):309\u201319.","journal-title":"J Am Stat Assoc"},{"issue":"3","key":"1391_CR25","doi-asserted-by":"publisher","first-page":"1","DOI":"10.18637\/jss.v023.i03","volume":"23","author":"C Erdman","year":"2007","unstructured":"Erdman C, Emerson JW, et al. bcp: an r package for performing a bayesian analysis of change point problems. J Stat Softw. 2007; 23(3):1\u201313.","journal-title":"J Stat Softw"},{"issue":"2","key":"1391_CR26","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1016\/S0304-4076(97)00115-2","volume":"86","author":"S Chib","year":"1998","unstructured":"Chib S. Estimation and comparison of multiple change-point models. Journal of econometrics. 1998; 86(2):221\u201341.","journal-title":"Journal of econometrics"},{"issue":"1","key":"1391_CR27","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1080\/07350015.1993.10509929","volume":"11","author":"JH Albert","year":"1993","unstructured":"Albert JH, Chib S. Bayes inference via gibbs sampling of autoregressive time series subject to markov mean and variance shifts. J Bus Econ Stat. 1993; 11(1):1\u201315.","journal-title":"J Bus Econ Stat"},{"key":"1391_CR28","doi-asserted-by":"crossref","unstructured":"Omranian N, Mueller-Roeber B, Nikoloski Z. Segmentation of biological multivariate time-series data. Scientific reports. 2015;5.","DOI":"10.1038\/srep08937"},{"key":"1391_CR29","doi-asserted-by":"publisher","first-page":"721","DOI":"10.1109\/TPAMI.1984.4767596","volume":"(6)","author":"S Geman","year":"1984","unstructured":"Geman S, Geman D. Stochastic relaxation, gibbs distributions, and the bayesian restoration of images. Pattern Analysis and Machine Intelligence, IEEE Transactions on Pattern Analysis and Machine Intelligence. 1984; (6):721\u2013741.","journal-title":"Pattern Analysis and Machine Intelligence, IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"issue":"3","key":"1391_CR30","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1080\/00031305.1992.10475878","volume":"46","author":"G Casella","year":"1992","unstructured":"Casella G, George EI. Explaining the gibbs sampler. Am Stat. 1992; 46(3):167\u201374.","journal-title":"Am Stat"},{"key":"1391_CR31","doi-asserted-by":"crossref","unstructured":"Kim C-J, Nelson CR. State-space models with regime switching. The MIT Press. 1999.","DOI":"10.7551\/mitpress\/6444.001.0001"},{"key":"1391_CR32","unstructured":"Judge GG, Hill RC, Griths W, Lutkepohl H, Lee T-C. Introduction to the theory and practice of econometrics. New York, John Wiley and Sons (Wiley Series in Probability and Mathematical Statistics). 1982."},{"key":"1391_CR33","volume-title":"Statistical Models in S, Chapter 8","author":"WS Cleveland","year":"1993","unstructured":"Cleveland WS, Grosse E, Shyu WM. Local regression models In: Chambers JM, Hastie TJ, editors. Statistical Models in S, Chapter 8. New York: Chapman & Hall: 1993. p. 309\u2013376."},{"issue":"2","key":"1391_CR34","doi-asserted-by":"publisher","first-page":"290","DOI":"10.1093\/jxb\/10.2.290","volume":"10","author":"F Richards","year":"1959","unstructured":"Richards F. A flexible growth function for empirical use. J Exp Bot. 1959; 10(2):290\u2013301.","journal-title":"J Exp Bot"},{"key":"1391_CR35","doi-asserted-by":"crossref","unstructured":"Efron B, Tibshirani RJ. An Introduction to the Bootstrap: CRC press; 1994.","DOI":"10.1201\/9780429246593"},{"issue":"1","key":"1391_CR36","first-page":"43","volume":"8","author":"SS Choi","year":"2010","unstructured":"Choi SS, Cha SH, Tappert CC. A survey of binary similarity and distance measures. J Syst Cybern Inform. 2010; 8(1):43\u20138.","journal-title":"J Syst Cybern Inform"},{"issue":"6","key":"1391_CR37","doi-asserted-by":"publisher","first-page":"1977","DOI":"10.1091\/mbc.02-02-0030","volume":"13","author":"ML Whitfield","year":"2002","unstructured":"Whitfield ML, Sherlock G, Saldanha AJ, Murray JI, Ball CA, Alexander KE, Matese JC, Perou CM, Hurt MM, Brown PO, et al. Identification of genes periodically expressed in the human cell cycle and their expression in tumors. Mol Biol Cell. 2002; 13(6):1977\u20132000.","journal-title":"Mol Biol Cell"},{"key":"1391_CR38","doi-asserted-by":"crossref","unstructured":"Tee A, Marshall GM, Liu PY, Liu T. In: Shimada H, (ed).Neuroblastoma: A Malignancy Due to Cell Differentiation Block: Neuroblastoma - Present and Future, InTech Open Access Publisher; 2012.","DOI":"10.5772\/27865"},{"issue":"5","key":"1391_CR39","doi-asserted-by":"publisher","first-page":"713","DOI":"10.1016\/j.chembiol.2013.03.020","volume":"20","author":"SM Frumm","year":"2013","unstructured":"Frumm SM, Fan ZP, Ross KN, Duvall JR, Gupta S, VerPlank L, Suh BC, Holson E, Wagner FF, Smith WB, et al. Selective hdac1\/hdac2 inhibitors induce neuroblastoma differentiation. Chem Biol. 2013; 20(5):713\u201325.","journal-title":"Chem Biol"},{"issue":"1","key":"1391_CR40","first-page":"219","volume":"49","author":"V Ciccarone","year":"1989","unstructured":"Ciccarone V, Spengler BA, Meyers MB, Biedler JL, Ross RA. Phenotypic diversification in human neuroblastoma cells: expression of distinct neural crest lineages. Cancer Res. 1989; 49(1):219\u201325.","journal-title":"Cancer Res"},{"key":"1391_CR41","volume-title":"R: A Language and Environment for Statistical Computing","author":"RC Team","year":"2016","unstructured":"Team RC. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing; 2016. R Foundation for Statistical Computing. https:\/\/www.R-project.org."},{"issue":"1","key":"1391_CR42","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1038\/nprot.2008.211","volume":"4","author":"DW Huang","year":"2009","unstructured":"Huang DW, Sherman BT, Lempicki RA. Systematic and integrative analysis of large gene lists using david bioinformatics resources. Nat Protoc. 2009; 4(1):44\u201357.","journal-title":"Nat Protoc"},{"issue":"1","key":"1391_CR43","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1093\/nar\/gkn923","volume":"37","author":"DW Huang","year":"2009","unstructured":"Huang DW, Sherman BT, Lempicki RA. Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res. 2009; 37(1):1\u201313.","journal-title":"Nucleic Acids Res"},{"key":"1391_CR44","unstructured":"DAVID: DAVID Bioinformatics Resources. https:\/\/david.ncifcrf.gov. Accessed: 2015-11-1."},{"issue":"2","key":"1391_CR45","doi-asserted-by":"publisher","first-page":"242","DOI":"10.1242\/dev.105445","volume":"142","author":"M Sim\u00f5es-Costa","year":"2015","unstructured":"Sim\u00f5es-Costa M, Bronner ME. Establishing neural crest identity: a gene regulatory recipe. Development. 2015; 142(2):242\u201357.","journal-title":"Development"},{"issue":"4","key":"1391_CR46","doi-asserted-by":"publisher","first-page":"511","DOI":"10.1002\/wsbm.1224","volume":"5","author":"DR Powell","year":"2013","unstructured":"Powell DR, Blasky AJ, Britt SG, Artinger KB. Riding the crest of the wave: parallels between the neural crest and cancer in epithelial-to-mesenchymal transition and migration. Wiley Interdiscip Rev Syst Biol Med. 2013; 5(4):511\u201322.","journal-title":"Wiley Interdiscip Rev Syst Biol Med"},{"issue":"2","key":"1391_CR47","doi-asserted-by":"publisher","first-page":"159","DOI":"10.1023\/A:1006171406740","volume":"41","author":"NF Schor","year":"1999","unstructured":"Schor NF. Neuroblastoma as a neurobiological disease. J Neuro-Oncol. 1999; 41(2):159\u201366.","journal-title":"J Neuro-Oncol"},{"issue":"3","key":"1391_CR48","first-page":"405","volume":"20","author":"M Katoh","year":"2007","unstructured":"Katoh M, Katoh M. Comparative integromics on non-canonical wnt or planar cell polarity signaling molecules: transcriptional mechanism of ptk7 in colorectal cancer and that of sema6a in undifferentiated es cells. Int J Mol Med. 2007; 20(3):405\u20139.","journal-title":"Int J Mol Med"},{"issue":"3","key":"1391_CR49","doi-asserted-by":"publisher","first-page":"279","DOI":"10.1038\/ng.2529","volume":"45","author":"TJ Pugh","year":"2013","unstructured":"Pugh TJ, Morozova O, Attiyeh EF, Asgharzadeh S, Wei JS, Auclair D, Carter SL, Cibulskis K, Hanna M, Kiezun A, et al. The genetic landscape of high-risk neuroblastoma. Nat Genet. 2013; 45(3):279\u201384.","journal-title":"Nat Genet"},{"issue":"1","key":"1391_CR50","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/1471-2105-12-1","volume":"12","author":"A Sinha","year":"2011","unstructured":"Sinha A, Markatou M. A platform for processing expression of short time series (pests). BMC Bioinforma. 2011; 12(1):1.","journal-title":"BMC Bioinforma"},{"issue":"7","key":"1391_CR51","doi-asserted-by":"publisher","first-page":"505","DOI":"10.1016\/j.ijmedinf.2005.03.014","volume":"74","author":"L Sacchi","year":"2005","unstructured":"Sacchi L, Bellazzi R, Larizza C, Magni P, Curk T, Petrovic U, Zupan B. Ta-clustering: Cluster analysis of gene expression profiles through temporal abstractions. Int J Med Inform. 2005; 74(7):505\u201317.","journal-title":"Int J Med Inform"},{"issue":"3","key":"1391_CR52","doi-asserted-by":"publisher","first-page":"430","DOI":"10.1093\/bioinformatics\/btm605","volume":"24","author":"P Magni","year":"2008","unstructured":"Magni P, Ferrazzi F, Sacchi L, Bellazzi R. Timeclust: a clustering tool for gene expression time series. Bioinformatics. 2008; 24(3):430\u20132.","journal-title":"Bioinformatics"},{"issue":"9","key":"1391_CR53","doi-asserted-by":"publisher","first-page":"1116","DOI":"10.1093\/bioinformatics\/btg047","volume":"19","author":"TR Hvidsten","year":"2003","unstructured":"Hvidsten TR, L\u00e6greid A, Komorowski J. Learning rule-based models of biological process from gene expression time profiles using gene ontology. Bioinformatics. 2003; 19(9):1116\u20131123.","journal-title":"Bioinformatics"}],"container-title":["BMC Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-016-1391-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12859-016-1391-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-016-1391-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,13]],"date-time":"2025-06-13T17:17:37Z","timestamp":1749835057000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcbioinformatics.biomedcentral.com\/articles\/10.1186\/s12859-016-1391-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,12,15]]},"references-count":53,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2016,12]]}},"alternative-id":["1391"],"URL":"https:\/\/doi.org\/10.1186\/s12859-016-1391-0","relation":{},"ISSN":["1471-2105"],"issn-type":[{"type":"electronic","value":"1471-2105"}],"subject":[],"published":{"date-parts":[[2016,12,15]]},"assertion":[{"value":"11 June 2016","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 November 2016","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 December 2016","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}],"article-number":"532"}}