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Network modeling and integrative analysis of high-dimensional genomic data


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Title: Network modeling and integrative analysis of high-dimensional genomic data
Other Titles: Nätverksmodellering och integrativ analys av högdimensionell genomikdata
Authors: Kallus, Jonatan
E-mail: kallus@chalmers.se
Issue Date: 7-May-2020
University: Göteborgs universitet. Naturvetenskapliga fakulteten
Institution: Department of Mathematical Sciences ; Institutionen för matematiska vetenskaper
Parts of work: 1. Kallus, J., Sánchez, J., Jauhiainen, A., Nelander, S., Jörnsten, R. (2017). ROPE: high-dimensional network modeling with robust control of edge FDR. Preprint arxiv.org/abs/1702.07685

2. Kallus, J., Johansson, P., Nelander, S., Jörnsten, R. (2019). MM-PCA: integrative analysis of multi-group and multi-view data. Preprint arxiv.org/abs/1911.04927

3. Kallus, J., Nelander, S., Jörnsten, R. (2020). Large-scale network estimation with structure-adaptive stability selection. Manuscript
Date of Defence: 2020-06-10
Disputation: Onsdagen den 10 juni 2020, kl. 13.15, Sal Pascal, Matematiska vetenskaper, Chalmers tvärgata 3
Degree: Doctor of Philosophy
Publication type: Doctoral thesis
Keywords: Mathmatical statistics
Biostatistics
Abstract: Genomic data describe biological systems on the molecular level and are, due to the immense diversity of life, high-dimensional. Network modeling and integrative analysis are powerful methods to interpret genomic data. However, network modeling is limited by the requirement to select model complexity and due to a bias towards biologically unrealistic network structures. Furthermore, there is a need to be able to integratively analyze data sets describing a wider range of different biological asp... more
ISBN: 978-91-7833-888-7
978-91-7833-889-4
URI: http://hdl.handle.net/2077/63747
Appears in Collections:Doctoral Theses from University of Gothenburg / Doktorsavhandlingar från Göteborgs universitet
Doctoral Theses / Doktorsavhandlingar Institutionen för matematiska vetenskaper

 

 

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