Clustering microarray gene expression data using enhanced harmony search

The DNA microarray technology concurrently monitors the expression levels of thousands of genes during significant biological processes and across the related samples. The better understanding of functional genomics is obtained by extracting the patterns hidden in gene expression data. It is handled by clustering which reveals natural structures and identify interesting patterns in the underlying data. In the proposed work clustering gene expression data is done through an enhanced harmony search EHS algorithm. Harmony search HS was inspired by the musical improvisation process where musicians improvise their instruments' pitches searching for a perfect state of harmony. In EHS the intensification and diversification process is incorporated in HS by smoothing the pitch values and replacing a fraction of instruments with new instruments. The experiment results are analysed with optimisation benchmark test functions and gene expression benchmark datasets. The results show that EHS outperforms HS in both benchmarks. Also this work determines the biological validation of the clusters with gene ontology in terms of function, process and component.

[1]  U. Alon,et al.  Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays. , 1999, Proceedings of the National Academy of Sciences of the United States of America.

[2]  K. Lee,et al.  A new structural optimization method based on the harmony search algorithm , 2004 .

[3]  J JacinthSalome,et al.  Efficient Clustering for Gene Expression Data , 2012 .

[4]  Chris H. Q. Ding,et al.  Analysis of gene expression profiles: class discovery and leaf ordering , 2002, RECOMB '02.

[5]  Zong Woo Geem,et al.  Harmony Search Optimization: Application to Pipe Network Design , 2002 .

[6]  Ivan G. Costa,et al.  Analyzing gene expression time-courses , 2005, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[7]  Vito Di Gesù,et al.  GenClust: A genetic algorithm for clustering gene expression data , 2005, BMC Bioinformatics.

[8]  Jarka Glassey,et al.  A novel methodology for finding the regulation on gene expression data , 2009 .

[9]  Rui Xu,et al.  Clustering Algorithms in Biomedical Research: A Review , 2010, IEEE Reviews in Biomedical Engineering.

[10]  Fazel Famili,et al.  Evaluation and optimization of clustering in gene expression data analysis , 2004, Bioinform..

[11]  Eckart Zitzler,et al.  An EA framework for biclustering of gene expression data , 2004, Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753).

[12]  D Haussler,et al.  Knowledge-based analysis of microarray gene expression data by using support vector machines. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

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

[14]  Xin Yao,et al.  An evolutionary clustering algorithm for gene expression microarray data analysis , 2006, IEEE Transactions on Evolutionary Computation.

[15]  Ash A. Alizadeh,et al.  Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling , 2000, Nature.

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

[17]  Ricardo J. G. B. Campello,et al.  Comparing Correlation Coefficients as Dissimilarity Measures for Cancer Classification in Gene Expression Data , 2011 .

[18]  Anil K. Jain,et al.  Data clustering: a review , 1999, CSUR.

[19]  Heather J. Ruskin,et al.  Techniques for clustering gene expression data , 2008, Comput. Biol. Medicine.

[20]  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.

[21]  Mu Zhu,et al.  A factor analysis model for functional genomics , 2005, BMC Bioinformatics.

[22]  G. C. Tseng,et al.  A comparative review of gene clustering in expression profile , 2004, ICARCV 2004 8th Control, Automation, Robotics and Vision Conference, 2004..