Post-graduate theses
Current Record: 4796 of 6695
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Identifier |
000366904 |
Title |
Estimation and control of the False Discovery Rate in Bayesian Network Skeleton Identification, with Application to Biological Data |
Alternative Title |
Εκτίμηση και έλεγχος του ρυθμού ψευδών ανακαλύψεων (FDR) στην ταυτοποίηση σκελετού Μπευσιανών δικτύων, με εφαρμογή σε βιολογικά δεδομένα |
Author
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Αρμέν, Αγγελος Πασχάλης
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Thesis advisor
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Τσαμαρδινός Ιωάννης
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Abstract |
Bayesian networks are graphical models that represent probabilistic relationships
among variables with extensive applications including biological data analysis.
In this work, we focus on the problem of estimating and controlling the
False Discovery Rate (FDR) in learning the skeleton (set of edges without regard
of direction) of a network. We present a unified approach to FDR estimation
and control in Bayesian network skeleton identification and experimentally evaluate
the performance of the most common FDR estimator in both tasks over
several networks and sample sizes. We employ simulated data as well as real
flow cytometry measurements of proteins and phospholipids in our evaluation.
We demonstrate that estimation in some cases is not conservative and strong
control is not achieved, while in other cases estimation is overly conservative.
After identifying the possible causes of this lack of accuracy, we evaluate several
approaches to deal with them. The results of these evaluations indicate that
the goal of accurately estimating and controlling the FDR in all cases using the
common FDR estimators may be unrealistic. Thus, we pursue the more realistic
goal of accurately estimating and controlling the FDR according to a relaxed
definition of false discovery. Our work opens new directions in the utilization
of the FDR in learning Bayesian network structure and in estimating structural
uncertainty in general.
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Language |
English |
Subject |
Bayesian Network |
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Biological Data Analysis |
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False Discovery Rate |
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Structure Learning |
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Ανάλυση βιολογικών δεδομένων |
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Εκμάθηση δομής |
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Μπευσιανα δίκτυα |
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Ρυθμός ψευδών ανακαλύψεων |
Issue date |
2011-07-15 |
Collection
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School/Department--School of Sciences and Engineering--Department of Computer Science--Post-graduate theses
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Type of Work--Post-graduate theses
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Permanent Link |
https://elocus.lib.uoc.gr//dlib/e/4/e/metadata-dlib-f13996598cd6a851c51817a914b9c829_1308555539.tkl
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Views |
736 |