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.

Microarray Gene Expression Data Pre-Processing Using Ppca and Classification Using Rf-SVM Algorithm

“…Saraswathi et al (2011) [13] Bioinspired evolutionary schemes are more appropriate and precise than the wrapper gene selection scheme [8] since they have the capability for searching and discovering the optimal or near-optimal solutions on high-dimensional solution spaces. In addition, they permit searching the solution space by means of considering more than one attribute simultaneously [8].…”

Implementation analyses of proteins and genes obtained from cancer patients

“…Recently, artificial neural networks, evolutionary computations, neighborhood-based methods, etc. are used for gene analysis and some of these methods have the potential to be applied to bioinformatics problems [9][10][11][12][13][14][15][16][17][18][19][20][21][22]. …”

Improved Feature Selection Based on Particle Swarm Optimization for Liver Disease Diagnosis

“…Granularity based grid search strategy is used to optimize the SVM model parameters. A combination of Integer-Coded Genetic Algorithm and PSO is coupled with the neural-network-based Extreme Learning Machine, is used for gene selection and cancer classification [27]. Semi supervised Ellipsoid ARTMAP algorithm combined with the PSO to distinguish tumor tissues with more than two categories through analyzing gene expression profiling is implemented [28].…”