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

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

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Issue 1, January 2011

IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB) : Volume 7

IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB) : Volume 6

IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB) : Volume 5

IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB) : Volume 4

IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB) : Volume 3

IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB) : Volume 2

IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB) : Volume 1

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

Content Provider	ACM Digital Library
Author	Zimmermann, Michael Saraswathi, Saras Sundararajan, Narasimhan Sundaram, Suresh Nilsen-hamilton, Marit
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.
Starting Page	452
Ending Page	463
Page Count	12
File Format	PDF
ISSN	15455963
DOI	10.1109/TCBB.2010.13
Volume Number	8
Issue Number	2
Journal	IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2011-03-01
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Biology and genetics, classifier design and evaluation, feature evaluation and selection, neural nets.
Content Type	Text
Resource Type	Article
Subject	Genetics Biotechnology Applied Mathematics

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

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

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

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