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Author: | Mohammadi, Pejman |
Title: | Bayesian integrative modelling of metabolic and transcriptional data using pathway information |
Publication type: | Master's thesis |
Publication year: | 2010 |
Pages: | vi + 54 Language: eng |
Department/School: | Informaatio- ja luonnontieteiden tiedekunta |
Main subject: | Informaatiotekniikka (T-61) |
Supervisor: | Kaski, Samuel |
Instructor: | Salojärvi, Jarkko |
OEVS: | Electronic archive copy is available via Aalto Thesis Database.
Instructions Reading digital theses in the closed network of the Aalto University Harald Herlin Learning CentreIn the closed network of Learning Centre you can read digital and digitized theses not available in the open network. The Learning Centre contact details and opening hours: https://learningcentre.aalto.fi/en/harald-herlin-learning-centre/ You can read theses on the Learning Centre customer computers, which are available on all floors.
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Location: | P1 Ark Aalto 6904 | Archive |
Keywords: | integrative modelling gene expression metabolic measurement stoichiometry metabolic network modelling bayesian analysis |
Abstract (eng): | One of the rising trends in computational systems biology is to characterize biological systems through integrated analysis of different sources of biological information. While gene expression and metabolic measurements are among the most prevalent biological information sources their integration is problematic due to the difficulties raised by the high dimensionality of the datasets, excessive noise and lack of data samples. The biochemical skeleton of metabolism has been extensively studied an widely used for simulating the metabolic behaviours in cells. Nevertheless, the rigid structure of metabolism can also be utilized as a scaffold for analysis of the genome-scale datasets. This helps to manage the high dimensionality and noise more effectively while also providing a natural link for integrating several omics datasets in the mean time. The ultimate goal of the work presented in this thesis is to develop a novel data fusion scheme by taking advantage of the genome-scale reconstructed models of metabolism as prior knowledge for integrated analysis of transcriptional and metabolic measurements. |
ED: | 2010-10-13 |
INSSI record number: 41073
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