A Graph-Theoretic Approach for Identifying Non-Redundant and Relevant Gene Markers from Microarray Data Using Multiobjective Binary PSO

The purpose of feature selection is to identify the relevant and non-redundant features from a dataset. In this article, the feature selection problem is organized as a graph-theoretic problem where a feature-dissimilarity graph is shaped from the data matrix. The nodes represent features and the ed...

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Published inPloS one Vol. 9; no. 3; p. e90949
Main Authors Mandal, Monalisa, Mukhopadhyay, Anirban
Format Journal Article
LanguageEnglish
Published United States Public Library of Science 13.03.2014
Public Library of Science (PLoS)
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Online AccessGet full text
ISSN1932-6203
1932-6203
DOI10.1371/journal.pone.0090949

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Abstract The purpose of feature selection is to identify the relevant and non-redundant features from a dataset. In this article, the feature selection problem is organized as a graph-theoretic problem where a feature-dissimilarity graph is shaped from the data matrix. The nodes represent features and the edges represent their dissimilarity. Both nodes and edges are given weight according to the feature's relevance and dissimilarity among the features, respectively. The problem of finding relevant and non-redundant features is then mapped into densest subgraph finding problem. We have proposed a multiobjective particle swarm optimization (PSO)-based algorithm that optimizes average node-weight and average edge-weight of the candidate subgraph simultaneously. The proposed algorithm is applied for identifying relevant and non-redundant disease-related genes from microarray gene expression data. The performance of the proposed method is compared with that of several other existing feature selection techniques on different real-life microarray gene expression datasets.
AbstractList The purpose of feature selection is to identify the relevant and non-redundant features from a dataset. In this article, the feature selection problem is organized as a graph-theoretic problem where a feature-dissimilarity graph is shaped from the data matrix. The nodes represent features and the edges represent their dissimilarity. Both nodes and edges are given weight according to the feature's relevance and dissimilarity among the features, respectively. The problem of finding relevant and non-redundant features is then mapped into densest subgraph finding problem. We have proposed a multiobjective particle swarm optimization (PSO)-based algorithm that optimizes average node-weight and average edge-weight of the candidate subgraph simultaneously. The proposed algorithm is applied for identifying relevant and non-redundant disease-related genes from microarray gene expression data. The performance of the proposed method is compared with that of several other existing feature selection techniques on different real-life microarray gene expression datasets.The purpose of feature selection is to identify the relevant and non-redundant features from a dataset. In this article, the feature selection problem is organized as a graph-theoretic problem where a feature-dissimilarity graph is shaped from the data matrix. The nodes represent features and the edges represent their dissimilarity. Both nodes and edges are given weight according to the feature's relevance and dissimilarity among the features, respectively. The problem of finding relevant and non-redundant features is then mapped into densest subgraph finding problem. We have proposed a multiobjective particle swarm optimization (PSO)-based algorithm that optimizes average node-weight and average edge-weight of the candidate subgraph simultaneously. The proposed algorithm is applied for identifying relevant and non-redundant disease-related genes from microarray gene expression data. The performance of the proposed method is compared with that of several other existing feature selection techniques on different real-life microarray gene expression datasets.
The purpose of feature selection is to identify the relevant and non-redundant features from a dataset. In this article, the feature selection problem is organized as a graph-theoretic problem where a feature-dissimilarity graph is shaped from the data matrix. The nodes represent features and the edges represent their dissimilarity. Both nodes and edges are given weight according to the feature’s relevance and dissimilarity among the features, respectively. The problem of finding relevant and non-redundant features is then mapped into densest subgraph finding problem. We have proposed a multiobjective particle swarm optimization (PSO)-based algorithm that optimizes average node-weight and average edge-weight of the candidate subgraph simultaneously. The proposed algorithm is applied for identifying relevant and non-redundant disease-related genes from microarray gene expression data. The performance of the proposed method is compared with that of several other existing feature selection techniques on different real-life microarray gene expression datasets.
Audience Academic
Author Mukhopadhyay, Anirban
Mandal, Monalisa
AuthorAffiliation University of Bonn, Bonn-Aachen International Center for IT, Germany
Department of Computer Science and Engineering, University of Kalyani, Kalyani, West Bengal, India
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BackLink https://www.ncbi.nlm.nih.gov/pubmed/24625895$$D View this record in MEDLINE/PubMed
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ContentType Journal Article
Copyright COPYRIGHT 2014 Public Library of Science
2014 Mandal, Mukhopadhyay. This is an open-access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
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Conceived and designed the experiments: AM MM. Performed the experiments: MM. Analyzed the data: MM. Contributed reagents/materials/analysis tools: AM. Wrote the paper: MM. Framework design: AM MM.
Competing Interests: The authors have declared that no competing interests exist.
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Snippet The purpose of feature selection is to identify the relevant and non-redundant features from a dataset. In this article, the feature selection problem is...
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StartPage e90949
SubjectTerms Algorithms
Biology
Biomarkers, Tumor - genetics
Computer Science
DNA microarrays
Engineering
Gene expression
Gene Expression Profiling - methods
Gene Expression Regulation, Neoplastic
Genes
Genetic Markers
Genetic research
Graph theory
Humans
Mathematics
Medicine
Models, Statistical
Models, Theoretical
Multiple objective analysis
Oligonucleotide Array Sequence Analysis - methods
Optimization
Optimization theory
Particle swarm optimization
Prostate cancer
Redundancy
Reproducibility of Results
Signal-To-Noise Ratio
Swarm intelligence
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Title A Graph-Theoretic Approach for Identifying Non-Redundant and Relevant Gene Markers from Microarray Data Using Multiobjective Binary PSO
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