An analysis of the use of probabilistic modeling for synaptic connectivity prediction from genomic data

An analysis of the use of probabilistic modeling for synaptic connectivity prediction from genomic data” by Roberto Santana, A. Mendiburu, and J. A. Lozano. In Proceedings of the 2012 Congress on Evolutionary Computation CEC-2012, (Brisbane, Australia), 2012, pp. 3221-3228.

Abstract

The identification of the specific genes that influence particular phenotypes is a common problem in genetic studies. In this paper we address the problem of determining the influence of gene joint expression in synapse predictability. The question is posed as an optimization problem in which the conditional entropy of gene subsets with respect to the synaptic connectivity phenotype is minimized. We investigate the use of single- and multi-objective estimation of distribution algorithms and focus on real data from C. elegans synaptic connectivity. We show that the introduced algorithms are able to compute gene sets that allow an accurate synapse predictability. However, the multi-objective approach can simultaneously search for gene sets with different number of genes. Our results also indicate that optimization problems defined on constrained binary spaces remain challenging for the conception of competitive estimation of distribution algorithm.

BibTeX entry:

@inproceedings{Santana_et_al:2012g,
   author = {Roberto Santana and A. Mendiburu and J. A. Lozano},
   title = {An analysis of the use of probabilistic modeling for synaptic
	connectivity prediction from genomic data},
   booktitle = {Proceedings of the 2012 Congress on Evolutionary
	Computation CEC-2012},
   pages = {3221--3228},
   publisher = {IEEE Press},
   address = {Brisbane, Australia},
   year = {2012},
   url = {http://dx.doi.org/10.1109/CEC.2012.6252997}
}

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