## Construction of Genomic Networks Using Mutual-Information Clustering and Reversible-Jump MCMC Predictor Design (2003)

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- [www.gensips.gatech.edu]
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### Other Repositories/Bibliography

Venue: | Signal Processing |

Citations: | 11 - 5 self |

### BibTeX

@INPROCEEDINGS{Zhou03constructionof,

author = {Xiaobo Zhou and Xiaodong Wang and Edward R. Dougherty},

title = {Construction of Genomic Networks Using Mutual-Information Clustering and Reversible-Jump MCMC Predictor Design},

booktitle = {Signal Processing},

year = {2003},

pages = {745--761}

}

### OpenURL

### Abstract

In this paper, we propose to construct the networks according to the following stages. Firstly, we determine the number of possible parent gene sets and the input sets of gene variables corresponding to each gene, and this is done by using a novel clustering technique based on mutual information minimization. Simulated annealing is employed to solve the optimization problem. After such initial gene clustering, we restrict our attention to the class of different functions from the possible parent gene sets to each target gene. Secondly, each function is then modelled by a perceptron consisting of a linear term and a nonlinear term. A reversible jump Markov chain Monte Carlo (MCMC) technique is used to calculate the model order and the parameters. Finally, coefficient of determination (CoD) is employed to compute the probability of selecting different predictors for each gene. To test this approach for constructing gene regulatory networks, we have carried out computational experiments using data from known gene response pathways including ionizing radiation and downstream targets of inactivating gene mutations.