We present a new reverse-engineering framework for gene regulatory network reconstruction. It works on temporal series of gene activation data and, using genetic programming, it extracts the activation functions of the different genes from those data. Successively, the gene regulatory network is reconstructed exploiting the automatic feature selection performed by genetic programming and its dynamics can be simulated using the previously extracted activation functions. The framework was tested on the well-known IRMA gene regulatory network, a simple network composed by five genes in the yeast Saccharomyces cerevisiae, defined in 2009 as a simplified biological model to benchmark reverse-engineering approaches. We show that the performances of the proposed framework on this benchmark network are encouraging

Farinaccio, A., Vanneschi, L., Provero, P., Mauri, G., Giacobini, M. (2011). A New Evolutionary Gene Regulatory Network Reverse Engineering Tool. In Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics 9th European Conference, EvoBIO 2011, Torino, Italy, April 27-29, 2011. Proceedings (pp.13-24). Berlin : Springer [10.1007/978-3-642-20389-3_2].

A New Evolutionary Gene Regulatory Network Reverse Engineering Tool

FARINACCIO, ANTONELLA;VANNESCHI, LEONARDO;MAURI, GIANCARLO;
2011

Abstract

We present a new reverse-engineering framework for gene regulatory network reconstruction. It works on temporal series of gene activation data and, using genetic programming, it extracts the activation functions of the different genes from those data. Successively, the gene regulatory network is reconstructed exploiting the automatic feature selection performed by genetic programming and its dynamics can be simulated using the previously extracted activation functions. The framework was tested on the well-known IRMA gene regulatory network, a simple network composed by five genes in the yeast Saccharomyces cerevisiae, defined in 2009 as a simplified biological model to benchmark reverse-engineering approaches. We show that the performances of the proposed framework on this benchmark network are encouraging
slide + paper
gene regulatory networks; automatic feature selection; random boolean networks
English
European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics
2011
Pizzuti, C; Ritchie, MD; Giacobini, M
Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics 9th European Conference, EvoBIO 2011, Torino, Italy, April 27-29, 2011. Proceedings
978-3-642-20388-6
2011
6623
13
24
none
Farinaccio, A., Vanneschi, L., Provero, P., Mauri, G., Giacobini, M. (2011). A New Evolutionary Gene Regulatory Network Reverse Engineering Tool. In Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics 9th European Conference, EvoBIO 2011, Torino, Italy, April 27-29, 2011. Proceedings (pp.13-24). Berlin : Springer [10.1007/978-3-642-20389-3_2].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/44830
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