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Found 5 swedish dissertations matching the above criteria.
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1. Comparative network analysis of human cancer: sparse graphical models with modular constraints and sample size correction
Abstract : In the study of transcriptional data for different groups (e.g. cancer types) it's reasonable to assume that some dependencies between genes on a transcriptional or genetic variants level are common across groups. Also, that this property is preserved locally, thus defining a modular structure in the model networks. READ MORE
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2. Network models with applications to genomic data: generalization, validation and uncertainty assessment
Abstract : The aim of this thesis is to provide a framework for the estimation and analysis of transcription networks in human cancer. The methods we develop are applied to data collected by The Cancer Genome Atlas (TCGA) and supporting simulations are based on derived models in order to reflect real data structure. READ MORE
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3. Towards Precision Medicine: Exploiting Genetic Variation in Tumours by Inferring Multitype Gene Regulatory Networks
Abstract : Precision medicine aims to customize treatment to a patient given measured genetic or other molecular data for diagnostics. In cancer, optimal medical treatment, depends on how far the disease has progressed, type and subtype of cancer and, the individual tumor’s circumstances. READ MORE
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4. In Pursuit of Ideal Model Selection for High-Dimensional Linear Regression
Abstract : The fundamental importance of model specification has motivated researchers to study different aspects of this problem. One of which is the task of model selection from the set of available competing models. READ MORE
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5. Experiment Design for Closed-loop System Identification with Applications in Model Predictive Control and Occupancy Estimation
Abstract : The objective of this thesis is to develop algorithms for application-oriented input design. This procedure takes the model application into account when designing experiments for system identification.This thesis is divided into two parts. READ MORE