Statistical methods for the analysis of complex omics data

Leiden Repository

Statistical methods for the analysis of complex omics data

Type: Doctoral Thesis
Title: Statistical methods for the analysis of complex omics data
Author: Tissier, R.
Issue Date: 2018-12-04
Keywords: Medical statistics
Family study
Genetic
Machine learning
Mixed models
Prediction
Data integration
Networks
Abstract: The major challenge in analysing omic datasets is the strong dependencies which are present between samples and features. Taking into account and modelling the different dependency structures can lead to further improvements of our knowledge of the biological mechanisms. Therefore, improving our ability to predict diseases. This dissertation focuses on the development of new statistical methods designed to take into account the existing structures inside omic datasets by using mixed models, Gaussian graphical models, and machine learning approaches.
Promotor: Supervisor: Houwing-Duistermaat J.J., Co-Supervisor: Rodríguez-Girondo M., Tsonaka S.
Faculty: Medicine / Leiden University Medical Center (LUMC)
University: Leiden
Uri: urn:isbn:9789402812398
Handle: http://hdl.handle.net/1887/67092
 

Files in this item

Description Size View
application/pdf Full Text 1.639Mb Under embargo until 2020-06-04
application/pdf Title Page_Contents 112.0Kb View/Open
application/pdf Chapter 01 499.3Kb View/Open
application/pdf Chapter 02 463.9Kb View/Open
application/pdf Chapter 03 376.0Kb Under embargo until 2020-06-04
application/pdf Chapter 04 230.4Kb View/Open
application/pdf Chapter 05 685.8Kb View/Open
application/pdf Chapter 06 564.3Kb Under embargo until 2020-06-04
application/pdf Bibliography 131.4Kb View/Open
application/pdf List of publications 102.9Kb View/Open
application/pdf Summary 92.06Kb View/Open
application/pdf Summary_in Dutch 93.37Kb View/Open
application/pdf Acknowledgements 87.35Kb View/Open
application/pdf CV 68.64Kb View/Open

This item appears in the following Collection(s)