Comparative study of two-layer particle swarm optimization and particle swarm optimization in classification for tumor gene expression data with different dimensionalities

Classification of gene expression data to determine different type or subtype of tumor samples is significantly important to research tumors in molecular biology level. Sample genes (dimensionalities) play a fundamental role in classification. Feature selection technologies used to reduce gene numbers and find informative genes have been presented in recent years. But the performance of feature selection in gene classification research is still controversial. In this study, a classification algorithm based on the two-layer particle swarm optimization (TLPSO) is established to classify the uncertain training sample sets obtained from three gene expression datasets which contain the leukemia, diffuse large B cell lymphoma (DLBCL) and multi-class tumors dataset respectively with the exponential increasing of gene numbers. Compared the results obtained by using the particle swarm optimization (PSO), the classification stability and accuracy of the results based on the proposed TLPSO classification algorithm is improved significantly and more information to clinicians for choosing more appropriate treatment can extracted.

[1]  Igor V. Tetko,et al.  Gene selection from microarray data for cancer classification - a machine learning approach , 2005, Comput. Biol. Chem..

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

[3]  Debashis Ghosh,et al.  Feature selection and molecular classification of cancer using genetic programming. , 2007, Neoplasia.

[4]  Leming Shi,et al.  Effect of training-sample size and classification difficulty on the accuracy of genomic predictors , 2010, Breast Cancer Research.

[5]  Yajie Liu,et al.  Classification of Leukemia Gene Expression Data Using Particle Swarm Optimization , 2012, 2012 Sixth International Conference on Genetic and Evolutionary Computing.

[6]  Chia-Chong Chen,et al.  Two-layer particle swarm optimization for unconstrained optimization problems , 2011, Appl. Soft Comput..

[7]  Heping Zhang,et al.  Cell and tumor classification using gene expression data: Construction of forests , 2003, Proceedings of the National Academy of Sciences of the United States of America.

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

[9]  Constantin F. Aliferis,et al.  A comprehensive evaluation of multicategory classification methods for microarray gene expression cancer diagnosis , 2004, Bioinform..

[10]  Sohail Asghar,et al.  A REVIEW OF FEATURE SELECTION TECHNIQUES IN STRUCTURE LEARNING , 2013 .

[11]  S. Mitra,et al.  Bioinformatics with soft computing , 2006, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[12]  Nello Cristianini,et al.  Support vector machine classification and validation of cancer tissue samples using microarray expression data , 2000, Bioinform..

[13]  Gregory Piatetsky-Shapiro,et al.  Microarray data mining: facing the challenges , 2003, SKDD.

[14]  Wei Xie,et al.  Accurate Cancer Classification Using Expressions of Very Few Genes , 2007, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[15]  T. Golub,et al.  Molecular profiling of diffuse large B-cell lymphoma identifies robust subtypes including one characterized by host inflammatory response. , 2004, Blood.

[16]  Ker-Chau Li,et al.  Exploring the within- and between-class correlation distributions for tumor classification , 2010, Proceedings of the National Academy of Sciences.

[17]  Kuldip K. Paliwal,et al.  Cancer classification by gradient LDA technique using microarray gene expression data , 2008, Data Knowl. Eng..

[18]  Carlos J. Alonso,et al.  Microarray gene expression classification with few genes: Criteria to combine attribute selection and classification methods , 2012, Expert Syst. Appl..

[19]  Jill P. Mesirov,et al.  Subclass Mapping: Identifying Common Subtypes in Independent Disease Data Sets , 2007, PloS one.

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