Reverse engineering of temporal Boolean networks from noisy data using evolutionary algorithms

by: Carlos Cotta, Jose M Troya

Neurocomputing, Vol. 62 (December 2004), pp. 111-129.

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Abstract

We consider the problem of inferring a genetic network from noisy data. This is done under the Temporal Boolean Network Model. Owing to the hardness of the problem, we propose an heuristic approach based on the combined utilization of evolutionary algorithms and other existing algorithms. The main features of this approach are the heuristic seeding of the initial population, the utilization of a specialized recombination operator, and the use of a majority-voting procedure in order to build a consensus solution. Experimental results provide support for the potential usefulness of this approach.

@article{citeulike:2099440, abstract = {We consider the problem of inferring a genetic network from noisy data. This is done under the Temporal Boolean Network Model. Owing to the hardness of the problem, we propose an heuristic approach based on the combined utilization of evolutionary algorithms and other existing algorithms. The main features of this approach are the heuristic seeding of the initial population, the utilization of a specialized recombination operator, and the use of a majority-voting procedure in order to build a consensus solution. Experimental results provide support for the potential usefulness of this approach.}, author = {Cotta, Carlos and Troya, Jose M. }, citeulike-article-id = {2099440}, doi = {http://dx.doi.org/10.1016/j.neucom.2003.12.007}, journal = {Neurocomputing}, keywords = {grn, network\_inference}, month = {December}, pages = {111--129}, posted-at = {2007-12-12 16:20:49}, priority = {2}, title = {Reverse engineering of temporal Boolean networks from noisy data using evolutionary algorithms}, url = {http://dx.doi.org/10.1016/j.neucom.2003.12.007}, volume = {62}, year = {2004} }

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TY - JOUR ID - citeulike:2099440 L3 - citeulike-article-id:2099440 N2 - We consider the problem of inferring a genetic network from noisy data. This is done under the Temporal Boolean Network Model. Owing to the hardness of the problem, we propose an heuristic approach based on the combined utilization of evolutionary algorithms and other existing algorithms. The main features of this approach are the heuristic seeding of the initial population, the utilization of a specialized recombination operator, and the use of a majority-voting procedure in order to build a consensus solution. Experimental results provide support for the potential usefulness of this approach. JF - Neurocomputing EP - 129 TI - Reverse engineering of temporal Boolean networks from noisy data using evolutionary algorithms VL - 62 SP - 111 KW - grn KW - network_inference AU - Cotta, Carlos AU - Troya, Jose PY - 2004/12// UR - http://dx.doi.org/10.1016/j.neucom.2003.12.007 ER -

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