Learning Bayesian Networks by Lamarckian Genetic Algorithm and Its Application to Yeast Cell-Cycle Gene Network Reconstruction from Time-Series Microarray Data

2004 ◽  
pp. 49-62 ◽  
Author(s):  
Sun-Chong Wang ◽  
Sai-Ping Li
Keyword(s):  
Genetic Algorithm ◽  
Cell Cycle ◽  
Time Series ◽  
Microarray Data ◽  
Network Reconstruction ◽  
Yeast Cell Cycle ◽  
Cell Cycle Gene ◽  
Gene Network Reconstruction ◽  
Lamarckian Genetic Algorithm ◽  
Time Series Microarray

scholarly journals Retraction Note to: Analyzing time-series microarray data reveals key genes in spinal cord injury

2015 ◽  
Vol 42 (10) ◽  
pp. 1485-1485
Author(s):  
Xun Xia ◽  
Bo Qu ◽  
Yuan Ma ◽  
Li-bin Yang ◽  
Hai-dong Huang ◽  
...  
Keyword(s):  
Spinal Cord ◽  
Spinal Cord Injury ◽  
Time Series ◽  
Microarray Data ◽  
Cord Injury ◽  
Key Genes ◽  
Time Series Microarray

Modelling Gene Regulatory Networks Using Computational Intelligence Techniques

2010 ◽  
pp. 244-265
Author(s):  
Ramesh Ram ◽  
Madhu Chetty
Keyword(s):  
Genetic Algorithm ◽  
Cell Cycle ◽  
Gene Regulatory Networks ◽  
Computational Intelligence ◽  
Regulatory Networks ◽  
Causal Model ◽  
Yeast Cell Cycle ◽  
Computational Perspective ◽  
Modelling Techniques ◽  
Gene Regulatory

This chapter presents modelling gene regulatory networks (GRNs) using probabilistic causal model and the guided genetic algorithm. The problem of modelling is explained from both a biological and computational perspective. Further, a comprehensive methodology for developing a GRN model is presented where the application of computation intelligence (CI) techniques can be seen to be significantly important in each phase of modelling. An illustrative example of the causal model for GRN modelling is also included and applied to model the yeast cell cycle dataset. The results obtained are compared for providing biological relevance to the findings which thereby underpins the CI based modelling techniques.

Cancer classification from time series microarray data through regulatory Dynamic Bayesian Networks

2020 ◽  
Vol 116 ◽  
pp. 103577 ◽  
Author(s):  
Konstantina Kourou ◽  
George Rigas ◽  
Costas Papaloukas ◽  
Michalis Mitsis ◽  
Dimitrios I. Fotiadis
Keyword(s):  
Time Series ◽  
Bayesian Networks ◽  
Microarray Data ◽  
Dynamic Bayesian Networks ◽  
Cancer Classification ◽  
Time Series Microarray
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