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Bayesian detection of periodic mRNA time profiles withouth use of training examples
Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, The Linnaeus Centre for Bioinformatics.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Genetics and Pathology. (Cancer Pharmacology and Informatics)
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Genetics and Pathology. Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Signal Processing.
2006 (English)In: BMC Bioinformatics, ISSN 1471-2105, E-ISSN 1471-2105, Vol. 7, 63- p.Article in journal (Refereed) Published
Description
Abstract [en]

BACKGROUND: Detection of periodically expressed genes from microarray data without use of known periodic and non-periodic training examples is an important problem, e.g. for identifying genes regulated by the cell-cycle in poorly characterised organisms. Commonly the investigator is only interested in genes expressed at a particular frequency that characterizes the process under study but this frequency is seldom exactly known. Previously proposed detector designs require access to labelled training examples and do not allow systematic incorporation of diffuse prior knowledge available about the period time. RESULTS: A learning-free Bayesian detector that does not rely on labelled training examples and allows incorporation of prior knowledge about the period time is introduced. It is shown to outperform two recently proposed alternative learning-free detectors on simulated data generated with models that are different from the one used for detector design. Results from applying the detector to mRNA expression time profiles from S. cerevisiae showsthat the genes detected as periodically expressed only contain a small fraction of the cell-cycle genes inferred from mutant phenotype. For example, when the probability of false alarm was equal to 7%, only 12% of the cell-cycle genes were detected. The genes detected as periodically expressed were found to have a statistically significant overrepresentation of known cell-cycle regulated sequence motifs. One known sequence motif and 18 putative motifs, previously not associated with periodic expression, were also over represented. CONCLUSION: In comparison with recently proposed alternative learning-free detectors for periodic gene expression, Bayesian inference allows systematic incorporation of diffuse a priori knowledge about, e.g. the period time. This results in relative performance improvements due to increased robustness against errors in the underlying assumptions. Results from applying the detector to mRNA expression time profiles from S. cerevisiae include several new findings that deserve further experimental studies.

Place, publisher, year, edition, pages
2006. Vol. 7, 63- p.
National Category
Medical and Health Sciences Engineering and Technology
Identifiers
URN: urn:nbn:se:uu:diva-96785DOI: 10.1186/1471-2105-7-63PubMedID: 16469110OAI: oai:DiVA.org:uu-96785DiVA: diva2:171473
Available from: 2008-02-20 Created: 2008-02-20 Last updated: 2016-09-25Bibliographically approved
In thesis
1. Fusing Domain Knowledge with Data: Applications in Bioinformatics
Open this publication in new window or tab >>Fusing Domain Knowledge with Data: Applications in Bioinformatics
2008 (English)Doctoral thesis, comprehensive summary (Other academic)
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.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2008. 55 p.
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 401
Keyword
Bioinformatics, cell cycle, cancer chemotherapy, predictive tests, performance estimation, bioinformatics, Bioinformatik
Identifiers
urn:nbn:se:uu:diva-8477 (URN)978-91-554-7094-4 (ISBN)
Public defence
2008-03-13, Fåhraeussalen, Rudbecklaboratoriet, hus C:5, Dag Hammarskjölds väg 20, Uppsala, 09:00 (English)
Opponent
Supervisors
Available from: 2008-02-20 Created: 2008-02-20 Last updated: 2009-05-12Bibliographically approved

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Isaksson, Anders

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