TY - JOUR

T1 - A GRASP metaheuristic for microarray data analysis

AU - Cordone, Roberto

AU - Lulli, Guglielmo

PY - 2013/12

Y1 - 2013/12

N2 - 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.

AB - 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.

KW - DNA microarray data

KW - Gene regulatory networks

KW - GRASP

KW - Path Relinking

KW - Tabu Search

U2 - 10.1016/j.cor.2012.10.008

DO - 10.1016/j.cor.2012.10.008

M3 - Journal article

AN - SCOPUS:84885954412

VL - 40

SP - 3108

EP - 3120

JO - Computers and Operations Research

JF - Computers and Operations Research

SN - 0305-0548

IS - 12

ER -