The Weighted Gene Regulatory Network (WGRN) problem consists in pruning a regulatory network obtained from DNA microarray gene expression data, in order to identify a reduced set of candidate elements which can explain the expression of all other genes. Since the problem appears to be particularly hard for general-purpose solvers, we develop a Greedy Randomized Adaptive Search Procedure (GRASP) and refine it with three alternative Path Relinking procedures. For comparison purposes, we also develop a Tabu Search algorithm with a self-adapting tabu tenure. The experimental results show that GRASP performs better than Tabu Search and that Path Relinking significantly contributes to its effectiveness. © 2012 Elsevier Ltd. All rights reserved.
Cordone, R., Lulli, G. (2013). A GRASP metaheuristic for microarray data analysis. COMPUTERS & OPERATIONS RESEARCH, 40, 3108-3120 [10.1016/j.cor.2012.10.008].
A GRASP metaheuristic for microarray data analysis
LULLI, GUGLIELMO
2013
Abstract
The Weighted Gene Regulatory Network (WGRN) problem consists in pruning a regulatory network obtained from DNA microarray gene expression data, in order to identify a reduced set of candidate elements which can explain the expression of all other genes. Since the problem appears to be particularly hard for general-purpose solvers, we develop a Greedy Randomized Adaptive Search Procedure (GRASP) and refine it with three alternative Path Relinking procedures. For comparison purposes, we also develop a Tabu Search algorithm with a self-adapting tabu tenure. The experimental results show that GRASP performs better than Tabu Search and that Path Relinking significantly contributes to its effectiveness. © 2012 Elsevier Ltd. All rights reserved.
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