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rdf:type |
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lifeskim:mentions |
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pubmed:dateCreated |
2011-6-15
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pubmed:abstractText |
Comparing metabolic profiles under different biological perturbations has become a powerful approach to investigating the functioning of cells. The profiles can be taken as single snapshots of a system, but more information is gained if they are measured longitudinally over time. The results are short time series consisting of relatively sparse data that cannot be analyzed effectively with standard time series techniques, such as autocorrelation and frequency domain methods. In this work, we study longitudinal time series profiles of glucose consumption in the yeast Saccharomyces cerevisiae under different temperatures and preconditioning regimens, which we obtained with methods of in vivo nuclear magnetic resonance (NMR) spectroscopy. For the statistical analysis we first fit several nonlinear mixed effect regression models to the longitudinal profiles and then used an ANOVA likelihood ratio method in order to test for significant differences between the profiles.
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pubmed:grant |
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pubmed:commentsCorrections |
http://linkedlifedata.com/resource/pubmed/commentcorrection/21518445-10397856,
http://linkedlifedata.com/resource/pubmed/commentcorrection/21518445-10708375,
http://linkedlifedata.com/resource/pubmed/commentcorrection/21518445-10849005,
http://linkedlifedata.com/resource/pubmed/commentcorrection/21518445-11102521,
http://linkedlifedata.com/resource/pubmed/commentcorrection/21518445-12782117,
http://linkedlifedata.com/resource/pubmed/commentcorrection/21518445-17259552,
http://linkedlifedata.com/resource/pubmed/commentcorrection/21518445-18388967,
http://linkedlifedata.com/resource/pubmed/commentcorrection/21518445-20377450,
http://linkedlifedata.com/resource/pubmed/commentcorrection/21518445-21088798,
http://linkedlifedata.com/resource/pubmed/commentcorrection/21518445-2242409,
http://linkedlifedata.com/resource/pubmed/commentcorrection/21518445-9687474
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pubmed:language |
eng
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pubmed:journal |
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pubmed:citationSubset |
IM
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pubmed:chemical |
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pubmed:status |
MEDLINE
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pubmed:issn |
1752-0509
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pubmed:author |
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pubmed:issnType |
Electronic
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pubmed:volume |
5
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pubmed:owner |
NLM
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pubmed:authorsComplete |
Y
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pubmed:pagination |
57
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pubmed:meshHeading |
pubmed-meshheading:21518445-Algorithms,
pubmed-meshheading:21518445-Carbon Isotopes,
pubmed-meshheading:21518445-Computational Biology,
pubmed-meshheading:21518445-Gene Expression Regulation, Fungal,
pubmed-meshheading:21518445-Glucose,
pubmed-meshheading:21518445-Magnetic Resonance Spectroscopy,
pubmed-meshheading:21518445-Models, Biological,
pubmed-meshheading:21518445-Models, Statistical,
pubmed-meshheading:21518445-Models, Theoretical,
pubmed-meshheading:21518445-Regression Analysis,
pubmed-meshheading:21518445-Saccharomyces cerevisiae,
pubmed-meshheading:21518445-Systems Biology,
pubmed-meshheading:21518445-Time Factors
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pubmed:year |
2011
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pubmed:articleTitle |
Statistical inference methods for sparse biological time series data.
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pubmed:affiliation |
Department of Bioinformatics and Biostatistics, School of Public Health and Information Sciences, University of Louisville, Louisville, KY 40202, USA.
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pubmed:publicationType |
Journal Article,
Research Support, U.S. Gov't, Non-P.H.S.,
Research Support, Non-U.S. Gov't,
Research Support, N.I.H., Extramural
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