Lineaarne mudel geeni ekspressiooni andmete analüüsi jaoks

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dc.contributor.advisorVilo, Jaaket
dc.contributor.authorTretjakov, Konstantinet
dc.contributor.otherTartu Ülikool. Matemaatika-informaatikateaduskondet
dc.contributor.otherTartu Ülikool. Arvutiteaduse instituutet
dc.date.accessioned2013-09-09T08:38:34Z
dc.date.available2013-09-09T08:38:34Z
dc.date.issued2005et
dc.description.abstractThe thesis proposes a novel method for the analysis of microarray data based on fitting a specific linear model that combines microarray data with DNA sequence information. The model is both descriptive and predictive: its coefficients provide insight into the structure of the genetic regulatory networks, and its predictive performance may be used to find a set of genes that play important role in transcription regulation (transcription factors). An efficient algorithm is proposed for calculating the least-squares fit for the parameters of the model. The proposed method is tested on a synthetic dataset and the results indicate that the approach is capable of detecting interesting relations in the data.et
dc.identifier.urihttp://hdl.handle.net/10062/32923
dc.language.isoenet
dc.publisherTartu Ülikoolet
dc.subject.otherbakalaureusetöödet
dc.subject.otherinformaatikaet
dc.subject.otherinfotehnoloogiaet
dc.subject.otherinformaticsen
dc.subject.otherinfotechnologyen
dc.titleLineaarne mudel geeni ekspressiooni andmete analüüsi jaokset
dc.title.alternativeA Linear Model of Genetic Transcription Regulation that Combines Microarray and Genome Sequence Dataet
dc.typeThesiset

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