|Data mining cDNA microarray experiment with a GEE approach
||The use of microarray technology provides access to the simultaneous expression
of thousands of genes and is revolutionizing the scientic community of functional
genomics. This thesis investigates a cDNA microarray experiment with the goal of
|Bayesian hierarchical regression model to detect quantitative trait loci
||Detecting genetic loci responsible for variation in quantitative traits is a problem
of great importance to biologists. The location on a genetic map responsible for
a quantitative trait is referred to as Quantitative Trait Loci, or QTL. This thesi...
||Metabolomics is the newest of the “-omics” sciences showing great potential in
identifying biomarkers for drug discovery. Since Metabolomics is a relatively new science there
are a few issues that have not been investigated to a great extent. As ad...
|Analysis of a hierarchial Bayesian method for quantitative trait loci
||Simulations were performed to compare two methods that detect quantitative trait loci on
plant data. Karl Broman’s interval mapping algorithm which uses only one observation value per
plant line was compared to a hierarchical Bayesian model that al...
|Time series forecasting competition among three sophisticated paradigms
||This paper is focused on assessing the performance of three kinds of forecasting paradigms: Dynamic Linear Models (DLM), Artificial Neural Networks (ANNs) and Autoregressive Integrated Moving Average models (ARIMA) in time series forecasting. We cont...
|QTL detection from stochastic process by Bayesian hierarchial regression model
||The problem of identifying the genetic loci contributing to variation in a quantitative
trait (called QTL) has been researched for a number of years, and is a growing
eld in statistical genetics. Most research focuses on the problem with only