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In vitro drug sensitivity-gene expression correlations involve a tissue of origin dependency
Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Biologiska sektionen, Institutionen för cell- och molekylärbiologi, Centrum för bioinformatik. Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för medicinska vetenskaper, Klinisk farmakologi. (Rolf Larsson)
Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för medicinska vetenskaper, Klinisk farmakologi. Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för genetik och patologi. (Rolf Larsson)
Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för medicinska vetenskaper, Klinisk farmakologi. (Rolf Larsson)
Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för medicinska vetenskaper, Klinisk farmakologi. (Rolf Larsson)
Vise andre og tillknytning
2007 (engelsk)Inngår i: Journal of chemical information and modeling, ISSN 1549-9596, Vol. 47, nr 1, s. 239-248Artikkel i tidsskrift (Fagfellevurdert) Published
Fritextbeskrivning
Abstract [en]

A major concern of chemogenomics is to associate drug activity with biological variables. Several reports have clustered cell line drug activity profiles as well as drug activity-gene expression correlation profiles and noted that the resulting groupings differ but still reflect mechanism of action. The present paper shows that these discrepancies can be viewed as a weighting of drug-drug distances, the weights depending on which cell lines the two drugs differ in.

sted, utgiver, år, opplag, sider
2007. Vol. 47, nr 1, s. 239-248
Emneord [en]
computers in chemistry, computer program
HSV kategori
Forskningsprogram
Elektroteknik med inriktning mot signalbehandling
Identifikatorer
URN: urn:nbn:se:uu:diva-20891DOI: 10.1021/ci060073nISI: 000243577400029PubMedID: 17238270OAI: oai:DiVA.org:uu-20891DiVA, id: diva2:48664
Tilgjengelig fra: 2007-10-28 Laget: 2007-10-28 Sist oppdatert: 2016-09-25bibliografisk kontrollert
Inngår i avhandling
1. Fusing Domain Knowledge with Data: Applications in Bioinformatics
Åpne denne publikasjonen i ny fane eller vindu >>Fusing Domain Knowledge with Data: Applications in Bioinformatics
2008 (engelsk)Doktoravhandling, med artikler (Annet vitenskapelig)
Abstract [en]

Massively parallel measurement techniques can be used for generating hypotheses about the molecular underpinnings of a biological systems. This thesis investigates how domain knowledge can be fused to data from different sources in order to generate more sophisticated hypotheses and improved analyses. We find our applications in the related fields of cell cycle regulation and cancer chemotherapy. In our cell cycle studies we design a detector of periodic expression and use it to generate hypotheses about transcriptional regulation during the course of the cell cycle in synchronized yeast cultures as well as investigate if domain knowledge about gene function can explain whether a gene is periodically expressed or not. We then generate hypotheses that suggest how periodic expression that depends on how the cells were perturbed into synchrony are regulated. The hypotheses suggest where and which transcription factors bind upstreams of genes that are regulated by the cell cycle. In our cancer chemotherapy investigations we first study how a method for identifiyng co-regulated genes associated with chemoresponse to drugs in cell lines is affected by domain knowledge about the genetic relationships between the cell lines. We then turn our attention to problems that arise in microarray based predictive medicine, were there typically are few samples available for learning the predictor and study two different means of alleviating the inherent trade-off betweeen allocation of design and test samples. First we investigate whether independent tests on the design data can be used for improving estimates of a predictors performance without inflicting a bias in the estimate. Then, motivated by recent developments in microarray based predictive medicine, we propose an algorithm that can use unlabeled data for selecting features and consequently improve predictor performance without wasting valuable labeled data.

sted, utgiver, år, opplag, sider
Uppsala: Acta Universitatis Upsaliensis, 2008. s. 55
Serie
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 401
Emneord
Bioinformatics, cell cycle, cancer chemotherapy, predictive tests, performance estimation, bioinformatics, Bioinformatik
Identifikatorer
urn:nbn:se:uu:diva-8477 (URN)978-91-554-7094-4 (ISBN)
Disputas
2008-03-13, Fåhraeussalen, Rudbecklaboratoriet, hus C:5, Dag Hammarskjölds väg 20, Uppsala, 09:00 (engelsk)
Opponent
Veileder
Tilgjengelig fra: 2008-02-20 Laget: 2008-02-20 Sist oppdatert: 2009-05-12bibliografisk kontrollert

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