Document Detail


Experimental design of time series data for learning from dynamic Bayesian networks.
MedLine Citation:
PMID:  17094245     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
Bayesian networks (BNs) and dynamic Bayesian networks (DBNs) are becoming more widely used as a way to learn various types of networks, including cellular signaling networks, from high-throughput data. Due to the high cost of performing experiments, we are interested in developing an experimental design for time series data generation. Specifically, we are interested in determining properties of time series data that make them more efficient for DBN modeling. We present a theoretical analysis on the ability of DBNs without hidden variables to learn from proteomic time series data. The analysis reveals, among other lessons, that under a reasonable set of assumptions a fixed budget is better spent on collecting many short time series data than on a few long time series data.
Authors:
David Page; Irene M Ong
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Publication Detail:
Type:  Journal Article    
Journal Detail:
Title:  Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing     Volume:  -     ISSN:  2335-6936     ISO Abbreviation:  Pac Symp Biocomput     Publication Date:  2006  
Date Detail:
Created Date:  2006-11-10     Completed Date:  2007-01-03     Revised Date:  2013-02-20    
Medline Journal Info:
Nlm Unique ID:  9711271     Medline TA:  Pac Symp Biocomput     Country:  Singapore    
Other Details:
Languages:  eng     Pagination:  267-78     Citation Subset:  IM    
Affiliation:
Department of Biostatistics & Medical Informatics, University of Wisconsin, Madison, WI 53706, USA. page@biostat.wisc.edu
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MeSH Terms
Descriptor/Qualifier:
Algorithms
Bayes Theorem*
Computational Biology
Proteomics / statistics & numerical data
Stochastic Processes

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


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