Insulin resistance involvement in prevalence of familial dysbetalipoproteinemia in ε2ε2 subjects by Bayesian network modeling

2018 ◽  
Vol 59 ◽  
pp. 31-36 ◽  
Author(s):  
James P. Corsetti ◽  
Tanzy M. Love ◽  
Charles E. Sparks ◽  
Stephan J.L. Bakker ◽  
Robin P.F. Dullaart
Keyword(s):  
Insulin Resistance ◽  
Bayesian Network ◽  
Network Modeling ◽  
Bayesian Network Modeling ◽  
Familial Dysbetalipoproteinemia

scholarly journals Bayesian Network Modeling to Improve Water Pricing Practices in Northwest China

Water ◽  
2015 ◽  
Vol 7 (10) ◽  
pp. 5617-5637 ◽  
Author(s):  
Yusuyunjiang Mamitimin ◽  
Til Feike ◽  
Reiner Doluschitz
Keyword(s):  
Bayesian Network ◽  
Network Modeling ◽  
Northwest China ◽  
Water Pricing ◽  
Bayesian Network Modeling

Bayesian Network Modeling of Transcription Factor Binding Sites

2007 ◽  
pp. 300-318
Author(s):  
Vipin Narang ◽  
Rajesh Chowdhary ◽  
Ankush Mittal ◽  
Wing-Kin Sung
Keyword(s):  
Transcription Factor ◽  
Parameter Estimation ◽  
Bayesian Network ◽  
Network Modeling ◽  
Network Models ◽  
Transcription Factor Binding Sites ◽  
Physical Problem ◽  
Network Applications ◽  
Bayesian Network Modeling ◽  
Bayesian Network Models

A predicament that engineers who wish to employ Bayesian networks to solve practical problems often face is the depth of study required in order to obtain a workable understanding of this tool. This chapter is intended as a tutorial material to assist the reader in efficiently understanding the fundamental concepts involved in Bayesian network applications. It presents a complete step by step solution of a bioinformatics problem using Bayesian network models, with detailed illustration of modeling, parameter estimation, and inference mechanisms. Considerations in determining an appropriate Bayesian network model representation of a physical problem are also discussed.

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