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A BAYESIAN STATISTICAL APPROACH TO MODELING GENE
REGULATORY PATHWAYS IN MICROARRAY DATA: Bayesian
networks are used to analyze time-series gene
expression placenta data. Preeclampsia is a health
condition which endangers both the mother and the
fetus and causes high rates of maternal mortality in
both the Developed and Developing worlds. The overall
goal of this study was to determine the gene
regulatory pathways that operate in the development
of the healthy human placenta. This study focused on
creating a Bayesian network to find the pathways
using a machine learning methodology. This study
showed that it is possible to predict via in-silico
analyses the gene regulatory pathways for 418 genes
associated with the development of the human
placenta. The software package used is the Weka
system from University of Waikato, New Zealand.
REGULATORY PATHWAYS IN MICROARRAY DATA: Bayesian
networks are used to analyze time-series gene
expression placenta data. Preeclampsia is a health
condition which endangers both the mother and the
fetus and causes high rates of maternal mortality in
both the Developed and Developing worlds. The overall
goal of this study was to determine the gene
regulatory pathways that operate in the development
of the healthy human placenta. This study focused on
creating a Bayesian network to find the pathways
using a machine learning methodology. This study
showed that it is possible to predict via in-silico
analyses the gene regulatory pathways for 418 genes
associated with the development of the human
placenta. The software package used is the Weka
system from University of Waikato, New Zealand.