Biomarker Discovery Based on Hybrid Optimization Algorithm and Artificial Neural Networks on Microarray Data for Cancer Classification

The improvement of high-through-put gene profiling based microarrays technology has provided monitoring the expression value of thousands of genes simultaneously. Detailed examination of changes in expression levels of genes can help physicians to have efficient diagnosing, classification of tumors and cancer′s types as well as effective treatments. Finding genes that can classify the group of cancers correctly based on hybrid optimization algorithms is the main purpose of this paper. In this paper, a hybrid particle swarm optimization and genetic algorithm method are used for gene selection and also artificial neural network (ANN) is adopted as the classifier. In this work, we have improved the ability of the algorithm for the classification problem by finding small group of biomarkers and also best parameters of the classifier. The proposed approach is tested on three benchmark gene expression data sets: Blood (acute myeloid leukemia, acute lymphoblastic leukemia), colon and breast datasets. We used 10-fold cross-validation to achieve accuracy and also decision tree algorithm to find the relation between the biomarkers for biological point of view. To test the ability of the trained ANN models to categorize the cancers, we analyzed additional blinded samples that were not previously used for the training procedure. Experimental results show that the proposed method can reduce the dimension of the data set and confirm the most informative gene subset and improve classification accuracy with best parameters based on datasets.

[1]  Russell C. Eberhart,et al.  A discrete binary version of the particle swarm algorithm , 1997, 1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation.

[2]  Kai Cao,et al.  A Learning Algorithm of Artificial Neural Network Based on GA - PSO , 2006, 2006 6th World Congress on Intelligent Control and Automation.

[3]  Seyed Mohammad Hosseini,et al.  A Novel Weighted Support Vector Machine Based on Particle Swarm Optimization for Gene Selection and Tumor Classification , 2012, Comput. Math. Methods Medicine.

[4]  P. Brown,et al.  Parallel human genome analysis: microarray-based expression monitoring of 1000 genes. , 1996, Proceedings of the National Academy of Sciences of the United States of America.

[5]  Jinn-Yi Yeh,et al.  Applying Data Mining Techniques for Cancer Classification from Gene Expression Data , 2007 .

[6]  M. Ringnér,et al.  Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks , 2001, Nature Medicine.

[7]  Russell C. Eberhart,et al.  A new optimizer using particle swarm theory , 1995, MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science.

[8]  Ronald W. Davis,et al.  Quantitative Monitoring of Gene Expression Patterns with a Complementary DNA Microarray , 1995, Science.

[9]  Shutao Li,et al.  Gene selection using hybrid particle swarm optimization and genetic algorithm , 2008, Soft Comput..

[10]  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.

[11]  Richard Simon,et al.  Microarray-based cancer prediction using single genes , 2011, BMC Bioinformatics.

[12]  Tianzi Jiang,et al.  A combinational feature selection and ensemble neural network method for classification of gene expression data , 2004, BMC Bioinformatics.

[13]  Li-Yeh Chuang,et al.  A hybrid feature selection method for DNA microarray data , 2011, Comput. Biol. Medicine.

[14]  Wei Kong,et al.  Hybrid particle swarm optimization and tabu search approach for selecting genes for tumor classification using gene expression data , 2008, Comput. Biol. Chem..

[15]  Li-Yeh Chuang,et al.  Gene selection and classification using Taguchi chaotic binary particle swarm optimization , 2011, Expert Syst. Appl..

[16]  R. Spang,et al.  Predicting the clinical status of human breast cancer by using gene expression profiles , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[17]  Victor Trevino,et al.  Compact cancer biomarkers discovery using a swarm intelligence feature selection algorithm , 2010, Comput. Biol. Chem..

[18]  Thomas A. Darden,et al.  Gene selection for sample classification based on gene expression data: study of sensitivity to choice of parameters of the GA/KNN method , 2001, Bioinform..

[19]  Toshiyuki Someya,et al.  Diagnostic classification of schizophrenia by neural network analysis of blood-based gene expression signatures , 2010, Schizophrenia Research.

[20]  Feng Chu,et al.  Applications of support vector machines to cancer classification with microarray data , 2005, Int. J. Neural Syst..

[21]  Y. Rahmat-Samii,et al.  Particle swarm, genetic algorithm, and their hybrids: optimization of a profiled corrugated horn antenna , 2002, IEEE Antennas and Propagation Society International Symposium (IEEE Cat. No.02CH37313).

[22]  Patrik Rydén,et al.  Classification of microarrays; synergistic effects between normalization, gene selection and machine learning , 2011, BMC Bioinformatics.

[23]  Li-Yeh Chuang,et al.  Improved binary PSO for feature selection using gene expression data , 2008, Comput. Biol. Chem..

[24]  Wei Kong,et al.  A combination of modified particle swarm optimization algorithm and support vector machine for gene selection and tumor classification. , 2007, Talanta.

[25]  Andrew Kusiak,et al.  Cancer gene search with data-mining and genetic algorithms , 2007, Comput. Biol. Medicine.

[26]  Li-Yeh Chuang,et al.  IG-GA: A Hybrid Filter/Wrapper Method for Feature Selection of Microarray Data , 2010 .

[27]  Nikhil R. Pal,et al.  Discovering biomarkers from gene expression data for predicting cancer subgroups using neural networks and relational fuzzy clustering , 2007, BMC Bioinformatics.

[28]  Chia-Feng Juang,et al.  A hybrid of genetic algorithm and particle swarm optimization for recurrent network design , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[29]  Dong-Ling Tong,et al.  Hybrid genetic algorithm-neural network: Feature extraction for unpreprocessed microarray data , 2011, Artif. Intell. Medicine.

[30]  Erwie Zahara,et al.  A hybrid genetic algorithm and particle swarm optimization for multimodal functions , 2008, Appl. Soft Comput..

[31]  J. Mesirov,et al.  Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. , 1999, Science.