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A multi-objective approach to discover biclusters in microarray data

Published: 07 July 2007 Publication History

Abstract

The main motivation for using a multi-objective evolutionary algorithm for finding biclusters in gene expression data is motivated by the fact that when looking for biclusters in gene expression matrix, several objectives have to be optimized simultaneously, and often these objectives are in conflict with each other. Moreover, the use of evolutionary computation is justified by the huge dimensionality of the search space, since it is known that evolutionary algorithms have great exploration power.
We focus our attention on finding biclusters of high quality with large variation. This is because, in expression data analysis, the most important goal may not be finding biclusters containing many genes and conditions, as it might be more interesting to find a set of genes showing similar behavior under a set of conditions. Experimental results confirm the validity of the proposed technique.

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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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Published: 07 July 2007

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Author Tags

  1. biclustering
  2. evolutionary algorithms
  3. gene expression data

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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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  • (2024)Type2 soft biclustering framework for Alzheimer microarrayApplied Soft Computing10.1016/j.asoc.2024.111227152(111227)Online publication date: Feb-2024
  • (2023)Metaheuristic Biclustering Algorithms: From State-of-the-art to Future OpportunitiesACM Computing Surveys10.1145/361759056:3(1-38)Online publication date: 6-Oct-2023
  • (2023)DeBic: A Differential Evolution Biclustering Algorithm for Microarray Data AnalysisArtificial Intelligence: Theories and Applications10.1007/978-3-031-28540-0_23(288-302)Online publication date: 18-Mar-2023
  • (2022)Water Consumption Pattern Analysis Using Biclustering: When, Why and HowWater10.3390/w1412195414:12(1954)Online publication date: 18-Jun-2022
  • (2022)Biclustering Algorithms Based on Metaheuristics: A ReviewMetaheuristics for Machine Learning10.1007/978-981-19-3888-7_2(39-71)Online publication date: 13-Aug-2022
  • (2021)Evolutionary Local Search Algorithm for the biclustering of gene expression data based on biological knowledgeApplied Soft Computing10.1016/j.asoc.2021.107177104:COnline publication date: 1-Jun-2021
  • (2019)Biclustering of Smart Building Electric Energy Consumption DataApplied Sciences10.3390/app90202229:2(222)Online publication date: 9-Jan-2019
  • (2019)A Hybrid Evolutionary Algorithm with Heuristic Mutation for Multi-objective Bi-clustering2019 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC.2019.8790309(2323-2330)Online publication date: Jun-2019
  • (2019)Automatic evolution of bi-clusters from microarray data using self-organized multi-objective evolutionary algorithmApplied Intelligence10.1007/s10489-019-01554-wOnline publication date: 28-Nov-2019
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