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Issue No. 02 - March/April (2011 vol. 8)
ISSN: 1545-5963
pp: 452-463
Saras Saraswathi , Iowa State University, Ames, IA
Suresh Sundaram , Indian Institute of Technology, New Delhi, India
Narasimhan Sundararajan , Nanyang Technological University, Singapore
Michael Zimmermann , Iowa State University, Ames, IA
Marit Nilsen-Hamilton , Iowa state University, Ames, IA
A combination of Integer-Coded Genetic Algorithm (ICGA) and Particle Swarm Optimization (PSO), coupled with the neural-network-based Extreme Learning Machine (ELM), is used for gene selection and cancer classification. ICGA is used with PSO-ELM to select an optimal set of genes, which is then used to build a classifier to develop an algorithm (ICGA_PSO_ELM) that can handle sparse data and sample imbalance. We evaluate the performance of ICGA-PSO-ELM and compare our results with existing methods in the literature. An investigation into the functions of the selected genes, using a systems biology approach, revealed that many of the identified genes are involved in cell signaling and proliferation. An analysis of these gene sets shows a larger representation of genes that encode secreted proteins than found in randomly selected gene sets. Secreted proteins constitute a major means by which cells interact with their surroundings. Mounting biological evidence has identified the tumor microenvironment as a critical factor that determines tumor survival and growth. Thus, the genes identified by this study that encode secreted proteins might provide important insights to the nature of the critical biological features in the microenvironment of each tumor type that allow these cells to thrive and proliferate.
Biology and genetics, classifier design and evaluation, feature evaluation and selection, neural nets.

S. Sundaram, M. Zimmermann, S. Saraswathi, M. Nilsen-Hamilton and N. Sundararajan, "ICGA-PSO-ELM Approach for Accurate Multiclass Cancer Classification Resulting in Reduced Gene Sets in Which Genes Encoding Secreted Proteins Are Highly Represented," in IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 8, no. , pp. 452-463, 2010.
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