Campus Event Calendar
Event Entry
What and Who
Stability analysis for oncogenetic trees
Jasmina Bogojeska
IMPRS
Master's Seminar
AG 1, AG 2, AG 3, AG 4, AG 5, SWS
MPI Audience
English
Note: We use this to send email in the morning.
Date, Time and Location
Wednesday, 31 May 2006
13:05
60 Minutes
E1 4
024
Saarbrücken
Abstract
One ultimate goal of biomedical research is personalized medicine which
uses genetic measurements from individual patients for an improved
diagnosis and therapy of diseases. Mixture models of oncogenetic trees can
be used to estimate the ordered accumulation of genetic events in disease
progression. From these evolutionary models, a genetic progression score
can be derived that estimates the genetic status of human tumors.
These mixture models have been previously implemented in the programming
language C. Building up on this code, we implement methods for estimation,
prediction and simulation of oncogenetic trees and for estimating the
genetic progression score as a package in the statistical programming
language R. This provides the biomedical community with an easy to use,
flexible, open access tool.
In the methodological part of the thesis, we analyze the stability of
estimated oncogenetic tree mixture models with bootstrap type methods.
First, measures for quantifying the variance in the input data, the
patterns of observed genetic events, and in the output data, in particular
the estimated genetic progression scores for single tumors, will be
compared and evaluated. Second, classical bootstrap methods will be used
to calculate confidence intervals for features derived from the estimated
tree models.
uses genetic measurements from individual patients for an improved
diagnosis and therapy of diseases. Mixture models of oncogenetic trees can
be used to estimate the ordered accumulation of genetic events in disease
progression. From these evolutionary models, a genetic progression score
can be derived that estimates the genetic status of human tumors.
These mixture models have been previously implemented in the programming
language C. Building up on this code, we implement methods for estimation,
prediction and simulation of oncogenetic trees and for estimating the
genetic progression score as a package in the statistical programming
language R. This provides the biomedical community with an easy to use,
flexible, open access tool.
In the methodological part of the thesis, we analyze the stability of
estimated oncogenetic tree mixture models with bootstrap type methods.
First, measures for quantifying the variance in the input data, the
patterns of observed genetic events, and in the output data, in particular
the estimated genetic progression scores for single tumors, will be
compared and evaluated. Second, classical bootstrap methods will be used
to calculate confidence intervals for features derived from the estimated
tree models.
Contact
Kerstin Kathy Meyer-Ross
226
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Jennifer Gerling, 05/29/2006 11:13 -- Created document.