{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T13:37:54Z","timestamp":1760708274127},"reference-count":31,"publisher":"Oxford University Press (OUP)","issue":"7","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2011,4,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Motivation: Analyzing short time-courses is a frequent and relevant problem in molecular biology, as, for example, 90% of gene expression time-course experiments span at most nine time-points. The biological or clinical questions addressed are elucidating gene regulation by identification of co-expressed genes, predicting response to treatment in clinical, trial-like settings or classifying novel toxic compounds based on similarity of gene expression time-courses to those of known toxic compounds. The latter problem is characterized by irregular and infrequent sample times and a total lack of prior assumptions about the incoming query, which comes in stark contrast to clinical settings and requires to implicitly perform a local, gapped alignment of time series. The current state-of-the-art method (SCOW) uses a variant of dynamic time warping and models time series as higher order polynomials (splines).<\/jats:p>\n               <jats:p>Results: We suggest to model time-courses monitoring response to toxins by piecewise constant functions, which are modeled as left\u2013right Hidden Markov Models. A Bayesian approach to parameter estimation and inference helps to cope with the short, but highly multivariate time-courses. We improve prediction accuracy by 7% and 4%, respectively, when classifying toxicology and stress response data. We also reduce running times by at least a factor of 140; note that reasonable running times are crucial when classifying response to toxins. In conclusion, we have demonstrated that appropriate reduction of model complexity can result in substantial improvements both in classification performance and running time.<\/jats:p>\n               <jats:p>Availability: A Python package implementing the methods described is freely available under the GPL from http:\/\/bioinformatics.rutgers.edu\/Software\/MVQueries\/.<\/jats:p>\n               <jats:p>Contact: \u00a0hafemeis@molgen.mpg.de; igcf@cin.ufpe.br; schliep@cs.rutgers.edu;<\/jats:p>\n               <jats:p>Supplementary information: \u00a0Supplementary data are available at Bioinformatics online.<\/jats:p>","DOI":"10.1093\/bioinformatics\/btr037","type":"journal-article","created":{"date-parts":[[2011,1,26]],"date-time":"2011-01-26T01:49:04Z","timestamp":1296006544000},"page":"946-952","source":"Crossref","is-referenced-by-count":15,"title":["Classifying short gene expression time-courses with Bayesian estimation of piecewise constant functions"],"prefix":"10.1093","volume":"27","author":[{"given":"Christoph","family":"Hafemeister","sequence":"first","affiliation":[{"name":"1 Department of Computational Molecular Biology, Max Planck Institute for Molecular Genetics, Berlin, Germany, 2Department of Computer Science and BioMaPS Institute for Quantitative Biology, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA, 3Center of Informatics, Federal University of Pernambuco, Recife, Brazil and 4Centrum Wiskunde & Informatica, 1098 XG Amsterdam, Netherlands"},{"name":"1 Department of Computational Molecular Biology, Max Planck Institute for Molecular Genetics, Berlin, Germany, 2Department of Computer Science and BioMaPS Institute for Quantitative Biology, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA, 3Center of Informatics, Federal University of Pernambuco, Recife, Brazil and 4Centrum Wiskunde & Informatica, 1098 XG Amsterdam, Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ivan G.","family":"Costa","sequence":"additional","affiliation":[{"name":"1 Department of Computational Molecular Biology, Max Planck Institute for Molecular Genetics, Berlin, Germany, 2Department of Computer Science and BioMaPS Institute for Quantitative Biology, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA, 3Center of Informatics, Federal University of Pernambuco, Recife, Brazil and 4Centrum Wiskunde & Informatica, 1098 XG Amsterdam, Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alexander","family":"Sch\u00f6nhuth","sequence":"additional","affiliation":[{"name":"1 Department of Computational Molecular Biology, Max Planck Institute for Molecular Genetics, Berlin, Germany, 2Department of Computer Science and BioMaPS Institute for Quantitative Biology, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA, 3Center of Informatics, Federal University of Pernambuco, Recife, Brazil and 4Centrum Wiskunde & Informatica, 1098 XG Amsterdam, Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alexander","family":"Schliep","sequence":"additional","affiliation":[{"name":"1 Department of Computational Molecular Biology, Max Planck Institute for Molecular Genetics, Berlin, Germany, 2Department of Computer Science and BioMaPS Institute for Quantitative Biology, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA, 3Center of Informatics, Federal University of Pernambuco, Recife, Brazil and 4Centrum Wiskunde & Informatica, 1098 XG Amsterdam, Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2011,1,25]]},"reference":[{"key":"2023012512180889500_B1","doi-asserted-by":"crossref","first-page":"2493","DOI":"10.1093\/bioinformatics\/bth283","article-title":"Analyzing time series gene expression data","volume":"20","author":"Bar-Joseph","year":"2004","journal-title":"Bioinformatics"},{"key":"2023012512180889500_B2","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1214\/aoms\/1177697196","article-title":"A maximization technique occurring in the statistical analysis of probabilistic functions of Markov chains","volume":"41","author":"Baum","year":"1970","journal-title":"Ann. 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