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An eScience-Bayes strategy for analyzing omics data
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.ORCID iD: 0000-0002-8083-2864
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
2010 (English)In: BMC Bioinformatics, ISSN 1471-2105, Vol. 11, 282- p.Article in journal (Refereed) Published
Abstract [en]

Background: The omics fields promise to revolutionize our understanding of biology and biomedicine. However, their potential is compromised by the challenge to analyze the huge datasets produced. Analysis of omics data is plagued by the curse of dimensionality, resulting in imprecise estimates of model parameters and performance. Moreover, the integration of omics data with other data sources is difficult to shoehorn into classical statistical models. This has resulted in ad hoc approaches to address specific problems. Results: We present a general approach to omics data analysis that alleviates these problems. By combining eScience and Bayesian methods, we retrieve scientific information and data from multiple sources and coherently incorporate them into large models. These models improve the accuracy of predictions and offer new insights into the underlying mechanisms. This "eScience-Bayes" approach is demonstrated in two proof-of-principle applications, one for breast cancer prognosis prediction from transcriptomic data and one for protein-protein interaction studies based on proteomic data. Conclusions: Bayesian statistics provide the flexibility to tailor statistical models to the complex data structures in omics biology as well as permitting coherent integration of multiple data sources. However, Bayesian methods are in general computationally demanding and require specification of possibly thousands of prior distributions. eScience can help us overcome these difficulties. The eScience-Bayes thus approach permits us to fully leverage on the advantages of Bayesian methods, resulting in models with improved predictive performance that gives more information about the underlying biological system.

Place, publisher, year, edition, pages
BioMed Central , 2010. Vol. 11, 282- p.
National Category
Pharmaceutical Sciences
URN: urn:nbn:se:uu:diva-109359DOI: 10.1186/1471-2105-11-282ISI: 000279732900004PubMedID: 20504364OAI: oai:DiVA.org:uu-109359DiVA: diva2:272073
Available from: 2009-10-14 Created: 2009-10-14 Last updated: 2015-05-04Bibliographically 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.
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Pharmacy, ISSN 1651-6192 ; 112
bioinformatics, high-throughout biology, eScience, model selection, model assessment
National Category
Bioinformatics and Systems Biology
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)
Available from: 2009-11-06 Created: 2009-10-15 Last updated: 2011-05-11Bibliographically approved

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Eklund, MartinSpjuth, OlaWikberg, Jarl
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