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A platform for the selection of genes in DNA microarraydata using evolutionary algorithms

Published: 07 July 2007 Publication History

Abstract

This paper presents a flexible framework to the task of featureselection in classification of DNA microarray data. Theuser can select a number of filter methods in the preprocessingstage and choose from a wide set of classifiers (models and algorithms from WEKA [17] are available) and accuracy estimation methods. This approach implements wrapper methods, where Evolutionary Algorithms, with variable sized set based representations are used to reduce the number of attributes. Two case studies were used to validate the approach, with three distinct classifiers (1-nearest neighbour, decision trees, SVMs), a filter method based on discriminant fuzzy patterns and k-fold cross-validation to estimate the generalization error.

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Cited By

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  • (2017)Membrane computing inspired feature selection model for microarray cancer dataIntelligent Data Analysis10.3233/IDA-17087521(S137-S157)Online publication date: 1-Apr-2017
  • (2015)Membrane Computing to Model Feature Selection of Microarray Cancer DataProceedings of the ASE BigData & SocialInformatics 201510.1145/2818869.2818885(1-9)Online publication date: 7-Oct-2015
  • (2015)New feature selection for gene expression classification based on degree of class overlap in principal dimensionsComputers in Biology and Medicine10.1016/j.compbiomed.2015.01.02264:C(292-298)Online publication date: 1-Sep-2015
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  1. A platform for the selection of genes in DNA microarraydata using evolutionary algorithms

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          cover image ACM Conferences
          GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation
          July 2007
          2313 pages
          ISBN:9781595936974
          DOI:10.1145/1276958
          Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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          Publication History

          Published: 07 July 2007

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          Author Tags

          1. bioinformatics
          2. dna microarrays
          3. feature selection
          4. wrapper methods

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          GECCO '07 Paper Acceptance Rate 266 of 577 submissions, 46%;
          Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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          Cited By

          View all
          • (2017)Membrane computing inspired feature selection model for microarray cancer dataIntelligent Data Analysis10.3233/IDA-17087521(S137-S157)Online publication date: 1-Apr-2017
          • (2015)Membrane Computing to Model Feature Selection of Microarray Cancer DataProceedings of the ASE BigData & SocialInformatics 201510.1145/2818869.2818885(1-9)Online publication date: 7-Oct-2015
          • (2015)New feature selection for gene expression classification based on degree of class overlap in principal dimensionsComputers in Biology and Medicine10.1016/j.compbiomed.2015.01.02264:C(292-298)Online publication date: 1-Sep-2015
          • (2010)Integrating Biological Information for Feature Selection in Microarray Data ClassificationProceedings of the 2010 Second International Conference on Computer Engineering and Applications - Volume 0210.1109/ICCEA.2010.215(330-334)Online publication date: 19-Mar-2010

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