ICGA-PSO-ELM Approach for Accurate Multiclass Cancer Classification Resulting in Reduced Gene Sets in Which Genes Encoding Secreted Proteins Are Highly Represented

Authors:
Saras Saraswathi;Suresh Sundaram;Narasimhan Sundararajan;Michael Zimmermann;Marit Nilsen-Hamilton
Affiliations:
Iowa State University, Ames, IA;Indian Institute of Technology, New Delhi, India;Nanyang Technological University, Singapore;Iowa State University, Ames, IA;Iowa state University, Ames, IA
Venue:
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Year:
2011

Citing 21
Cited 4

Genetic algorithms + data structures = evolution programs (3rd ed.)

Genetic algorithms + data structures = evolution programs (3rd ed.)
Gene Selection for Cancer Classification using Support Vector Machines

Machine Learning
Microarray data mining: facing the challenges

ACM SIGKDD Explorations Newsletter
GOstat: find statistically overrepresented Gene Ontologies within a group of genes

Bioinformatics
A comprehensive evaluation of multicategory classification methods for microarray gene expression cancer diagnosis

Bioinformatics
Multiclass cancer classification and biomarker discovery using GA-based algorithms

Bioinformatics
Accurate Cancer Classification Using Expressions of Very Few Genes

IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Development of Two-Stage SVM-RFE Gene Selection Strategy for Microarray Expression Data Analysis

IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Multicategory Classification Using An Extreme Learning Machine for Microarray Gene Expression Cancer Diagnosis

IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
MSVM-RFE

Bioinformatics
An integrated algorithm for gene selection and classification applied to microarray data of ovarian cancer

Artificial Intelligence in Medicine
A review of feature selection techniques in bioinformatics

Bioinformatics
A sequential multi-category classifier using radial basis function networks

Neurocomputing
Risk-sensitive loss functions for sparse multi-category classification problems

Information Sciences: an International Journal
A probabilistic multi-class strategy of one-vs.-rest support vector machines for cancer classification

Neurocomputing
No-reference image quality assessment using modified extreme learning machine classifier

Applied Soft Computing
Optimal Aggregation of Binary Classifiers for Multiclass Cancer Diagnosis Using Gene Expression Profiles

IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Gene selection from microarray data for cancer classification-a machine learning approach

Computational Biology and Chemistry
Multiclass microarray data classification using GA/ANN method

PRICAI'06 Proceedings of the 9th Pacific Rim international conference on Artificial intelligence
Performance enhancement of extreme learning machine for multi-category sparse data classification problems

Engineering Applications of Artificial Intelligence
The particle swarm - explosion, stability, and convergence in amultidimensional complex space

IEEE Transactions on Evolutionary Computation

Particle swarm optimization: a powerful family of stochastic optimizers. analysis, design and application to inverse modelling

ICSI'11 Proceedings of the Second international conference on Advances in swarm intelligence - Volume Part I
2012 Special Issue: Identification of brain regions responsible for Alzheimer's disease using a Self-adaptive Resource Allocation Network

Neural Networks
A modified artificial fish swarm algorithm for the optimization of extreme learning machines

ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part II
A simulation to analyze feature selection methods utilizing gene ontology for gene expression classification

Journal of Biomedical Informatics

Quantified Score

Hi-index	0.00

Visualization

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.