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Ridge-SimSel: A generalization of the variable selection method SimSel to multicollinear data sets
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Mathematics, Mathematical Statistics.
(English)Article in journal (Refereed) Submitted
National Category
Probability Theory and Statistics Medical and Health Sciences
Identifiers
URN: urn:nbn:se:uu:diva-109361OAI: oai:DiVA.org:uu-109361DiVA: diva2:272077
Available from: 2009-10-14 Created: 2009-10-14 Last updated: 2012-07-26Bibliographically approved
In thesis
1. eScience Approaches to Model Selection and Assessment: Applications in Bioinformatics
Open this publication in new window or tab >>eScience Approaches to Model Selection and Assessment: Applications in Bioinformatics
2009 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

High-throughput experimental methods, such as DNA and protein microarrays, have become ubiquitous and indispensable tools in biology and biomedicine, and the number of high-throughput technologies is constantly increasing. They provide the power to measure thousands of properties of a biological system in a single experiment and have the potential to revolutionize our understanding of biology and medicine. However, the high expectations on high-throughput methods are challenged by the problem to statistically model the wealth of data in order to translate it into concrete biological knowledge, new drugs, and clinical practices. In particular, the huge number of properties measured in high-throughput experiments makes statistical model selection and assessment exigent. To use high-throughput data in critical applications, it must be warranted that the models we construct reflect the underlying biology and are not just hypotheses suggested by the data. We must furthermore have a clear picture of the risk of making incorrect decisions based on the models.

The rapid improvements of computers and information technology have opened up new ways of how the problem of model selection and assessment can be approached. Specifically, eScience, i.e. computationally intensive science that is carried out in distributed network envi- ronments, provides computational power and means to efficiently access previously acquired scientific knowledge. This thesis investigates how we can use eScience to improve our chances of constructing biologically relevant models from high-throughput data. Novel methods for model selection and assessment that leverage on computational power and on prior scientific information to "guide" the model selection to models that a priori are likely to be relevant are proposed. In addition, a software system for deploying new methods and make them easily accessible to end users is presented.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2009. 51 p.
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Pharmacy, ISSN 1651-6192 ; 112
Keyword
bioinformatics, high-throughout biology, eScience, model selection, model assessment
National Category
Bioinformatics and Systems Biology
Identifiers
urn:nbn:se:uu:diva-109437 (URN)978-91-554-7634-2 (ISBN)
Public defence
2009-11-28, B42, BMC, Husargatan 3, Uppsala, 10:15 (English)
Opponent
Supervisors
Available from: 2009-11-06 Created: 2009-10-15 Last updated: 2011-05-11Bibliographically approved

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Eklund, Martin

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