Abstract
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
Current Bioinformatics
Title:Combining Quantum-Behaved PSO and K2 Algorithm for Enhancing Gene Network Construction
Volume: 8 Issue: 1
Author(s): Zhihua Du, Yingying Zhu and Weixiang Liu
Affiliation:
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
Abstract: 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.
Export Options
About this article
Cite this article as:
Du Zhihua, Zhu Yingying and Liu Weixiang, Combining Quantum-Behaved PSO and K2 Algorithm for Enhancing Gene Network Construction, Current Bioinformatics 2013; 8 (1) . https://dx.doi.org/10.2174/1574893611308010017
DOI https://dx.doi.org/10.2174/1574893611308010017 |
Print ISSN 1574-8936 |
Publisher Name Bentham Science Publisher |
Online ISSN 2212-392X |
- Author Guidelines
- Graphical Abstracts
- Fabricating and Stating False Information
- Research Misconduct
- Post Publication Discussions and Corrections
- Publishing Ethics and Rectitude
- Increase Visibility of Your Article
- Archiving Policies
- Peer Review Workflow
- Order Your Article Before Print
- Promote Your Article
- Manuscript Transfer Facility
- Editorial Policies
- Allegations from Whistleblowers
Related Articles
-
Noncovalent Binding to DNA: Still a Target in Developing Anticancer Agents
Current Medicinal Chemistry Targeting Key Transporters in Tumor Glycolysis as a Novel Anticancer Strategy
Current Topics in Medicinal Chemistry Monitoring T Cell Responses to Cancer Immunotherapy: Can We Now Identify Biomarkers Predicting Patients Who will be Responders
Current Cancer Therapy Reviews Plant Coumestans: Recent Advances and Future Perspectives in Cancer Therapy
Anti-Cancer Agents in Medicinal Chemistry De Novo Design of New Inhibitor of Mutated Tyrosine-Kinase for the Myeloid Leukemia Treatment
Current Pharmaceutical Design Systemic and Biophase Bioavailability and Pharmacokinetics of Nanoparticulate Drug Delivery Systems
Current Drug Delivery Insights Into Nicotinic Receptor Signaling in Nicotine Addiction: Implications for Prevention and Treatment
Current Neuropharmacology Current Perspectives on the Role of Nrf2 in 5-Fluorouracil Resistance in Colorectal Cancer
Anti-Cancer Agents in Medicinal Chemistry Cellular and Humoral Responses following Minimally Invasive Surgery: Role of Reactive Oxygen Species
Current Metabolomics Simultaneous Determination of Cytochrome P450 Oxidation Capacity in Humans: A Review on the Phenotyping Cocktail Approach
Current Pharmaceutical Biotechnology The Multidrug Resistance Mechanisms and their Interactions with the Radiopharmaceutical Probes Used for an In Vivo Detection
Current Drug Metabolism TRAIL Agonists on Clinical Trials for Cancer Therapy: The Promises and the Challenges
Reviews on Recent Clinical Trials Meet the Editorial Board:
Protein & Peptide Letters Challenges & Outcome of Thoracic Surgery in a Resource Constrained Developing African Country
Current Respiratory Medicine Reviews Update of QSAR & Docking & Alignment Studies of the DNA Polymerase Inhibitors
Current Bioinformatics Current Status and Perspectives Regarding the Therapeutic Potential of Targeting EGFR Pathway by Curcumin in Lung Cancer
Current Pharmaceutical Design Could Growth Factor-Mediated Extracellular Matrix Deposition and Degradation Offer the Ground for Directed Pharmacological Targeting in Fibrosarcoma?
Current Medicinal Chemistry Engineering of Plant Metabolism for Drug and Food
Current Medicinal Chemistry - Immunology, Endocrine & Metabolic Agents Eosinophils in Cancer: Favourable or Unfavourable?
Current Medicinal Chemistry Lipid Nanoparticles to Deliver miRNA in Cancer
Current Pharmaceutical Biotechnology