title: ICGA-PSO-ELM approach for accurate multiclass cancer classification resulting in reduced gene sets in which genes encoding secreted proteins are highly represented.
abstract: 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.
authors.affiliation: Iowa State University, Ames, IA, USA. saras@iastate.edu
authors.forename: Saras
authors.initials: S
authors.lastname: Saraswathi
authors.forename: Suresh
authors.initials: S
authors.lastname: Sundaram
authors.forename: Narasimhan
authors.initials: N
authors.lastname: Sundararajan
authors.forename: Michael
authors.initials: M
authors.lastname: Zimmermann
authors.forename: Marit
authors.initials: M
authors.lastname: Nilsen-Hamilton
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D020869:Gene Expression Profiling / Q000379:methods; D053263:Gene Regulatory Networks; D006801:Humans; D009363:Neoplasm Proteins / Q000235:genetics* / Q000378:metabolism; D009369:Neoplasms / Q000145:classification* / Q000235:genetics*; D010363:Pattern Recognition, Automated
chemical_list: D009363:Neoplasm Proteins