Visualization and Structure Learning of Gene Regulatory Networks using Bayesian Networks
Date Issued
2009
Author(s)
Ristevski, Blagoj
Abstract
The cell functions and development are regulated by
complex networks of genes, proteins and other components by
means of their mutual interactions. These networks are called
gene regulatory networks (GRNs). The gene regulatory networks
are used to reveal the fundamental gene regulatory mechanisms,
to determine the reasons for many diseases and interactions
between drugs and their targets, to produce a clear and
comprehensible notion for cell regulation,. The introduction of
experimental technologies such as microarrays and chromatin
immunoprecipitation ChIP-chip, has provided a large number of
available datasets related to gene expression and transcription
factors (TFs). These datasets are basis for further analysis to
reveal the gene regulation mechanisms. We implemented and
visualized the dynamic Bayesian network which is able to cope
with missing data and can include a prior knowledge about
transcription factors. Also, we describe the obtained results and
survey the common structure learning algorithms for learning of
GRN’s structure.
complex networks of genes, proteins and other components by
means of their mutual interactions. These networks are called
gene regulatory networks (GRNs). The gene regulatory networks
are used to reveal the fundamental gene regulatory mechanisms,
to determine the reasons for many diseases and interactions
between drugs and their targets, to produce a clear and
comprehensible notion for cell regulation,. The introduction of
experimental technologies such as microarrays and chromatin
immunoprecipitation ChIP-chip, has provided a large number of
available datasets related to gene expression and transcription
factors (TFs). These datasets are basis for further analysis to
reveal the gene regulation mechanisms. We implemented and
visualized the dynamic Bayesian network which is able to cope
with missing data and can include a prior knowledge about
transcription factors. Also, we describe the obtained results and
survey the common structure learning algorithms for learning of
GRN’s structure.
Subjects
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