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Combining Quantum-Behaved PSO and K2 Algorithm for Enhancing Gene Network Construction

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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.
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Keywords: CONSTURCTION GENE NETWORK; Component; DNA microarray technology; K2 ALGORITHM; acyclic graph; gene networks; optimization algorithm; quantum-behaved particle swarm optimization (QPSO); root nodes; structure learning

Document Type: Research Article

Publication date: 01 February 2013

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  • Current Bioinformatics aims to publish all the latest and outstanding developments in bioinformatics. Each issue contains a series of timely, in-depth reviews written by leaders in the field, covering a wide range of the integration of biology with computer and information science.

    The journal focuses on reviews on advances in computational molecular/structural biology, encompassing areas such as computing in biomedicine and genomics, computational proteomics and systems biology, and metabolic pathway engineering. Developments in these fields have direct implications on key issues related to health care, medicine, genetic disorders, development of agricultural products, renewable energy, environmental protection, etc.

    Current Bioinformatics is an essential journal for all academic and industrial researchers who want expert knowledge on all major advances in bioinformatics.
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