Combining Quantum-Behaved PSO and K2 Algorithm for Enhancing Gene Network Construction
Construction of the gene regulatory networks is a challenged problem in systems biology and bioinformatics.
This paper presents construction of gene network using combined quantum-behaved PSO and K2 algorithm. Recent
studies have shown that Bayesian Network is an effective way to learn the network structure. K2 algorithm is widely used
because of its heuristic searching techniques and fast convergence, but it suffers from local optima. And the performance
of K2 algorithm is greatly affected by a prior ordering of input nodes. Quantum-behaved PSO is a population-based
stochastic search process, which automatically searches for the optimal solution in the search space. So, we combined it
with K2 algorithm for construction gene network. The results of hybrid PSO, K2 (we refer to it as QPSO-K2 algorithm),
stand-alone K2 and quantum-behaved PSO algorithms are compared on several datasets. Among the three algorithms, the
hybrid QPSO-K2 algorithm performs well for all of the datasets.
Keywords: Component, gene networks, quantum-behaved particle swarm optimization (QPSO), structure learning, K2 ALGORITHM, CONSTURCTION GENE NETWORK, acyclic graph, DNA microarray technology, root nodes, optimization algorithm
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