Campus Event Calendar
Event Entry
What and Who
Recovery of Regulatory Networks from High-Throughput Data
Harald Steck
MIT
Talk
AG 1, AG 2, AG 3, AG 4
MPI Audience
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Date, Time and Location
Wednesday, 18 June 2003
14:00
-- Not specified --
46.1 - MPII
024
Saarbrücken
Abstract
Transcriptional regulatory networks describe how the various genes in a
genome regulate each other's activity on a transcriptional level. This
talk is concerned with the recovery of transcriptional regulatory networks
from high-throughput data sources (e.g., gene expression data, location
data). The main challenges are the development of models that can
describe the specific biological process involved in gene regulation, and
to relate these models to (different kinds of) data, using statistical
methods. For simplicity, we use Bayesian networks as a (simple)
statistical model that can capture the (conditional) statistical
dependences that arise from biological processes involved in gene
regulation. Focusing on the statistical methods for recovering the
network structure, we find that the use of *principled* statistical
methods is essential for obtaining meaningful network structures from
high-throughput data. This is not surprising, since the available data
is very noisy and extremely small compared to the complexity of biological
systems. We derive such principled statistical methods that are necessary
for various tasks involved in the recovery of network structures. We
illustrate in our experiments that our principled methods lead to results
that are completely different from those obtained by combining
off-the-shelf methods.
genome regulate each other's activity on a transcriptional level. This
talk is concerned with the recovery of transcriptional regulatory networks
from high-throughput data sources (e.g., gene expression data, location
data). The main challenges are the development of models that can
describe the specific biological process involved in gene regulation, and
to relate these models to (different kinds of) data, using statistical
methods. For simplicity, we use Bayesian networks as a (simple)
statistical model that can capture the (conditional) statistical
dependences that arise from biological processes involved in gene
regulation. Focusing on the statistical methods for recovering the
network structure, we find that the use of *principled* statistical
methods is essential for obtaining meaningful network structures from
high-throughput data. This is not surprising, since the available data
is very noisy and extremely small compared to the complexity of biological
systems. We derive such principled statistical methods that are necessary
for various tasks involved in the recovery of network structures. We
illustrate in our experiments that our principled methods lead to results
that are completely different from those obtained by combining
off-the-shelf methods.
Contact
Ruth Schneppen-Christmann
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