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dc.contributorVirginia Techen
dc.contributor.authorFalin, Lee J.en
dc.contributor.authorTyler, Brett M.en
dc.date.accessioned2014-06-17T20:12:05Zen
dc.date.available2014-06-17T20:12:05Zen
dc.date.issued2011-07-22en
dc.identifier.citationFalin LJ, Tyler BM. Using interpolation to estimate system uncertainty in gene expression experiments. PLoS One. 2011;6:e22071.  http://www.ncbi.nlm.nih.gov/pubmed/21799771en
dc.identifier.issn1932-6203en
dc.identifier.urihttp://hdl.handle.net/10919/48983en
dc.description.abstractThe widespread use of high-throughput experimental assays designed to measure the entire complement of a cell's genes or gene products has led to vast stores of data that are extremely plentiful in terms of the number of items they can measure in a single sample, yet often sparse in the number of samples per experiment due to their high cost. This often leads to datasets where the number of treatment levels or time points sampled is limited, or where there are very small numbers of technical and/or biological replicates. Here we introduce a novel algorithm to quantify the uncertainty in the unmeasured intervals between biological measurements taken across a set of quantitative treatments. The algorithm provides a probabilistic distribution of possible gene expression values within unmeasured intervals, based on a plausible biological constraint. We show how quantification of this uncertainty can be used to guide researchers in further data collection by identifying which samples would likely add the most information to the system under study. Although the context for developing the algorithm was gene expression measurements taken over a time series, the approach can be readily applied to any set of quantitative systems biology measurements taken following quantitative (i.e. non-categorical) treatments. In principle, the method could also be applied to combinations of treatments, in which case it could greatly simplify the task of exploring the large combinatorial space of future possible measurements.en
dc.description.sponsorshipThis work was supported in part by grants from the National Science Foundation #DBI-0211863, from the Agriculture and Food Research Initiative of the National Institute of Food and Agriculture of the USDA #2005-35604-15525, by the Virginia Bioinformatics Institute, and by the Virginia Tech Graduate Program in Genetics, Bioinformatics and Computational Biology. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.en
dc.language.isoen_USen
dc.publisherPublic Library of Scienceen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectAlgorithmsen
dc.subjectBiologistsen
dc.subjectConfidence intervalsen
dc.subjectGene expressionen
dc.subjectInterpolationen
dc.subjectMonte Carlo methoden
dc.subjectProbability distributionen
dc.subjectTime measurementen
dc.titleUsing Interpolation to Estimate System Uncertainty in Gene Expression Experimentsen
dc.typeArticle - Refereeden
dc.identifier.urlhttp://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0022071en
dc.date.accessed2014-05-01en
dc.title.serialPLoS ONEen
dc.identifier.doihttps://doi.org/10.1371/journal.pone.0022071en


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