Gene expression data analysis with the clustering method based on an improved quantum-behaved Particle Swarm Optimization

Microarray technology has been widely applied in study of measuring gene expression levels for thousands of genes simultaneously. In this technology, gene cluster analysis is useful for discovering the function of gene because co-expressed genes are likely to share the same biological function. Many clustering algorithms have been used in the field of gene clustering. This paper proposes a new scheme for clustering gene expression datasets based on a modified version of Quantum-behaved Particle Swarm Optimization (QPSO) algorithm, known as the Multi-Elitist QPSO (MEQPSO) model. The proposed clustering method also employs a one-step K-means operator to effectively accelerate the convergence speed of the algorithm. The MEQPSO algorithm is tested and compared with some other recently proposed PSO and QPSO variants on a suite of benchmark functions. Based on the computer simulations, some empirical guidelines have been provided for selecting the suitable parameters of MEQPSO clustering. The performance of MEQPSO clustering algorithm has been extensively compared with several optimization-based algorithms and classical clustering algorithms over several artificial and real gene expression datasets. Our results indicate that MEQPSO clustering algorithm is a promising technique and can be widely used for gene clustering.

[1]  Ron Shamir,et al.  Clustering Gene Expression Patterns , 1999, J. Comput. Biol..

[2]  James Kennedy,et al.  Defining a Standard for Particle Swarm Optimization , 2007, 2007 IEEE Swarm Intelligence Symposium.

[3]  Maurice Clerc,et al.  The particle swarm - explosion, stability, and convergence in a multidimensional complex space , 2002, IEEE Trans. Evol. Comput..

[4]  D. Botstein,et al.  Cluster analysis and display of genome-wide expression patterns. , 1998, Proceedings of the National Academy of Sciences of the United States of America.

[5]  Anbupalam Thalamuthu,et al.  Gene expression Evaluation and comparison of gene clustering methods in microarray analysis , 2006 .

[6]  Wenbo Xu,et al.  An improved quantum-behaved particle swarm optimization algorithm with weighted mean best position , 2008, Appl. Math. Comput..

[7]  George C Tseng,et al.  Tight Clustering: A Resampling‐Based Approach for Identifying Stable and Tight Patterns in Data , 2005, Biometrics.

[8]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[9]  Jun Sun,et al.  A global search strategy of quantum-behaved particle swarm optimization , 2004, IEEE Conference on Cybernetics and Intelligent Systems, 2004..

[10]  Wenbo Xu,et al.  Quantum-behaved particle swarm optimization with a hybrid probability distribution , 2006 .

[11]  G. Church,et al.  Systematic determination of genetic network architecture , 1999, Nature Genetics.

[12]  Zohar Yakhini,et al.  Clustering gene expression patterns , 1999, J. Comput. Biol..

[13]  Tieli Sun,et al.  An efficient hybrid data clustering method based on K-harmonic means and Particle Swarm Optimization , 2009, Expert Syst. Appl..

[14]  Michael Ruogu Zhang,et al.  Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization. , 1998, Molecular biology of the cell.

[15]  P. J. Angeline,et al.  Using selection to improve particle swarm optimization , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

[16]  Adrian E. Raftery,et al.  Model-based clustering and data transformations for gene expression data , 2001, Bioinform..

[17]  Russ B. Altman,et al.  Missing value estimation methods for DNA microarrays , 2001, Bioinform..

[18]  Amin Safari,et al.  Tuning of damping controller for UPFC using quantum particle swarm optimizer , 2010 .

[19]  Amit Konar,et al.  Automatic kernel clustering with a Multi-Elitist Particle Swarm Optimization Algorithm , 2008, Pattern Recognit. Lett..

[20]  Jing J. Liang,et al.  Problem Definitions and Evaluation Criteria for the CEC 2005 Special Session on Real-Parameter Optimization , 2005 .

[21]  Andries Petrus Engelbrecht,et al.  Data clustering using particle swarm optimization , 2003, The 2003 Congress on Evolutionary Computation, 2003. CEC '03..

[22]  S. N. Omkar,et al.  Quantum behaved Particle Swarm Optimization (QPSO) for multi-objective design optimization of composite structures , 2009, Expert Syst. Appl..

[23]  Aly A. Farag,et al.  A modified fuzzy c-means algorithm for bias field estimation and segmentation of MRI data , 2002, IEEE Transactions on Medical Imaging.

[24]  Zhaohui S. Qin,et al.  Clustering microarray gene expression data using weighted Chinese restaurant process , 2006, Bioinform..

[25]  Yi Lu,et al.  Incremental genetic K-means algorithm and its application in gene expression data analysis , 2004, BMC Bioinformatics.

[26]  Lehrstuhl für Elektrische,et al.  Gaussian swarm: a novel particle swarm optimization algorithm , 2004, IEEE Conference on Cybernetics and Intelligent Systems, 2004..

[27]  M. Clerc,et al.  The swarm and the queen: towards a deterministic and adaptive particle swarm optimization , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[28]  J. Mesirov,et al.  Interpreting patterns of gene expression with self-organizing maps: methods and application to hematopoietic differentiation. , 1999, Proceedings of the National Academy of Sciences of the United States of America.

[29]  Michael K. Ng,et al.  An Entropy Weighting k-Means Algorithm for Subspace Clustering of High-Dimensional Sparse Data , 2007, IEEE Transactions on Knowledge and Data Engineering.

[30]  Songfeng Lu,et al.  Short-term combined economic emission hydrothermal scheduling using improved quantum-behaved particle swarm optimization , 2010, Expert Syst. Appl..

[31]  Yongji Wang,et al.  A new improved Quantum-behaved Particle Swarm Optimization model , 2009, 2009 4th IEEE Conference on Industrial Electronics and Applications.

[32]  L. Coelho.  A quantum particle swarm optimizer with chaotic mutation operator , 2008 .

[33]  Michele Leone,et al.  Clustering by Soft-constraint Affinity Propagation: Applications to Gene-expression Data , 2022 .

[34]  M. Narasimha Murty,et al.  Genetic K-means algorithm , 1999, IEEE Trans. Syst. Man Cybern. Part B.

[35]  Yuhui Shi,et al.  Particle swarm optimization: developments, applications and resources , 2001, Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546).

[36]  P. Rousseeuw.  Silhouettes: a graphical aid to the interpretation and validation of cluster analysis , 1987 .

[37]  J. Barker,et al.  Large-scale temporal gene expression mapping of central nervous system development. , 1998, Proceedings of the National Academy of Sciences of the United States of America.

[38]  Zhen Ji,et al.  PK-means: A new algorithm for gene clustering , 2008, Comput. Biol. Chem..

[39]  Roger E Bumgarner,et al.  Integrated genomic and proteomic analyses of a systematically perturbed metabolic network. , 2001, Science.

[40]  Leandro dos Santos Coelho,et al.  Gaussian quantum-behaved particle swarm optimization approaches for constrained engineering design problems , 2010, Expert Syst. Appl..

[41]  Wenbo Xu,et al.  A Novel and More Efficient Search Strategy of Quantum-Behaved Particle Swarm Optimization , 2007, ICANNGA.

[42]  Michael K. Ng,et al.  Automated variable weighting in k-means type clustering , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[43]  Ka Yee Yeung,et al.  Validating clustering for gene expression data , 2001, Bioinform..

[44]  Zhang Zhisheng.  Short Communication: Quantum-behaved particle swarm optimization algorithm for economic load dispatch of power system , 2010 .

[45]  Roger E Bumgarner,et al.  Clustering gene-expression data with repeated measurements , 2003, Genome Biology.

[46]  Ujjwal Maulik,et al.  An improved algorithm for clustering gene expression data , 2007, Bioinform..

[47]  Delbert Dueck,et al.  Clustering by Passing Messages Between Data Points , 2007, Science.

[48]  Erwie Zahara,et al.  A hybridized approach to data clustering , 2008, Expert Syst. Appl..

[49]  Wenbo Xu,et al.  Adaptive parameter control for quantum-behaved particle swarm optimization on individual level , 2005, 2005 IEEE International Conference on Systems, Man and Cybernetics.

[50]  D. Botstein,et al.  The transcriptional program in the response of human fibroblasts to serum. , 1999, Science.

[51]  Wenbo Xu,et al.  Particle swarm optimization with particles having quantum behavior , 2004, Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753).

[52]  Frans van den Bergh,et al.  An analysis of particle swarm optimizers , 2002 .

[53]  James Kennedy,et al.  Bare bones particle swarms , 2003, Proceedings of the 2003 IEEE Swarm Intelligence Symposium. SIS'03 (Cat. No.03EX706).

[54]  Qi Wu,et al.  Cauchy mutation for decision-making variable of Gaussian particle swarm optimization applied to parameters selection of SVM , 2011, Expert Syst. Appl..

[55]  Xu Wen-bo,et al.  Convergence analysis of quantum-behaved particle swarm optimization algorithm and study on its control parameter , 2010 .

[56]  James C. Bezdek,et al.  Clustering with a genetically optimized approach , 1999, IEEE Trans. Evol. Comput..

[57]  Leandro dos Santos Coelho,et al.  MESFET DC model parameter extraction using Quantum Particle Swarm Optimization , 2009, Microelectron. Reliab..

[58]  Anil K. Jain,et al.  Algorithms for Clustering Data , 1988 .

[59]  Jing J. Liang,et al.  Comprehensive learning particle swarm optimizer for global optimization of multimodal functions , 2006, IEEE Transactions on Evolutionary Computation.

[60]  Tzay-Farn Shih.  Particle Swarm Optimization Algorithm for Energy-Efficient Cluster-Based Sensor Networks , 2006, IEICE Trans. Fundam. Electron. Commun. Comput. Sci..