A graph-theoretic approach for identifying non-redundant and relevant gene markers from microarray data using multiobjective binary PSO (Q30000690)

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English	A graph-theoretic approach for identifying non-redundant and relevant gene markers from microarray data using multiobjective binary PSO	scientific article (publication date: 2014)

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instance of

scholarly article

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title

A graph-theoretic approach for identifying non-redundant and relevant gene markers from microarray data using multiobjective binary PSO (English)

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author name string

Monalisa Mandal

series ordinal

1

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Anirban Mukhopadhyay

series ordinal

2

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language of work or name

English

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publication date

2014

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published in

PLOS One

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volume

9

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issue

3

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page(s)

e90949

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copyright license

Creative Commons Attribution 4.0 International

start time

13 March 2014

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stated in

April 2022 Public Data File from Crossref

copyright status

copyrighted

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cites work

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Minimum redundancy feature selection from microarray gene expression data

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Nonparametric methods for identifying differentially expressed genes in microarray data

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Translation of microarray data into clinically relevant cancer diagnostic tests using gene expression ratios in lung cancer and mesothelioma

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Particle swarm optimization for feature selection in classification: a multi-objective approach.

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21 January 2018

Orthogonal forward selection and backward elimination algorithms for feature subset selection

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A fast and elitist multiobjective genetic algorithm: NSGA-II

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Treatment-specific changes in gene expression discriminate in vivo drug response in human leukemia cells

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Wrappers for feature subset selection

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Improved gene selection for classification of microarrays

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reference URL

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retrieved

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inferred from PubMed ID database lookup

Identifiers

DOI

10.1371/JOURNAL.PONE.0090949

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ADS bibcode

2014PLoSO...990949M

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PMC publication ID

3953335

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PubMed publication ID

24625895

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ResearchGate publication ID

260809058

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