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GECCO
2007
Springer
558views Optimization» more  GECCO 2007»
14 years 3 months ago
A chain-model genetic algorithm for Bayesian network structure learning
Bayesian Networks are today used in various fields and domains due to their inherent ability to deal with uncertainty. Learning Bayesian Networks, however is an NP-Hard task [7]....
Ratiba Kabli, Frank Herrmann, John McCall
BIOSYSTEMS
2007
111views more  BIOSYSTEMS 2007»
13 years 9 months ago
A Markovian approach to the control of genetic regulatory networks
This paper presents an approach for controlling gene networks based on a Markov chain model, where the state of a gene network is represented as a probability distribution, while ...
Peter C. Y. Chen, Jeremy W. Chen
IJAR
2006
89views more  IJAR 2006»
13 years 8 months ago
Learning probabilistic decision graphs
Probabilistic decision graphs (PDGs) are a representation language for probability distributions based on binary decision diagrams. PDGs can encode (context-specific) independence...
Manfred Jaeger, Jens D. Nielsen, Tomi Silander
BMCBI
2010
172views more  BMCBI 2010»
13 years 9 months ago
Comparison of evolutionary algorithms in gene regulatory network model inference
Background: The evolution of high throughput technologies that measure gene expression levels has created a data base for inferring GRNs (a process also known as reverse engineeri...
Alina Sîrbu, Heather J. Ruskin, Martin Crane
ICANN
2003
Springer
14 years 2 months ago
Finite Mixture Model of Bounded Semi-naive Bayesian Networks Classifier
Kaizhu Huang, Irwin King, Michael R. Lyu