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Evolutionary computation for biclustering of gene expression

Published: 13 March 2005 Publication History

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No abstract available.

References

[1]
A. A. Alizadeh and et al. Distinct types of diffuse large b-cell lymphoma identified by gene expression profiling. Nature, 403:503--511, 200.
[2]
Y. Cheng and G. M. Church. Biclustering of expression data. In Proceedings of the 8th International Conference on Itelligent Systems for Molecular Biology (ISMB'00), pages 93--103, 2000.

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Giuseppina Carla Gini

This short paper from the Symposium on Applied Computing illustrates an algorithm, called SEBI, for solving the biclustering problem of gene analysis. The algorithm introduces evolutionary computation into the basic schema of such methods, which basically are optimization methods. Inserting randomness in the process seems to give better results in terms of the stability of the method. The details in the paper are not enough to make it reproducible. However, it seems to point in the direction of exploiting stochastic methods in areas where basic statistic methods are considered inadequate. Online Computing Reviews Service

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cover image ACM Conferences
SAC '05: Proceedings of the 2005 ACM symposium on Applied computing
March 2005
1814 pages
ISBN:1581139640
DOI:10.1145/1066677
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: 13 March 2005

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SAC05
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SAC05: The 2005 ACM Symposium on Applied Computing
March 13 - 17, 2005
New Mexico, Santa Fe

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Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

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  • (2016)Local Optima Avoidance in GA Biclustering using Map ReduceInternational Journal of Knowledge Discovery in Bioinformatics10.4018/IJKDB.20160101046:1(37-47)Online publication date: 1-Jan-2016
  • (2016)A Novel Evolutionary Biclustering Approach using MapReduceEBC-MRInternational Journal of Knowledge Discovery in Bioinformatics10.4018/IJKDB.20160101036:1(26-36)Online publication date: 1-Jan-2016
  • (2015)Measuring the quality of linear patterns in biclustersMethods10.1016/j.ymeth.2015.04.00583(18-27)Online publication date: Jul-2015
  • (2008)Gene Expression Data Analysis Using a Novel Approach to Biclustering Combining Discrete and Continuous DataIEEE/ACM Transactions on Computational Biology and Bioinformatics10.1109/TCBB.2007.702515:4(583-593)Online publication date: 1-Oct-2008
  • (2007)BALBOA: Extending Bicluster Analysis to Classify ORFs using Expression Data2007 IEEE 7th International Symposium on BioInformatics and BioEngineering10.1109/BIBE.2007.4375679(995-1002)Online publication date: Oct-2007
  • (2006)Application of Simulated Annealing to the Biclustering of Gene Expression DataIEEE Transactions on Information Technology in Biomedicine10.1109/TITB.2006.87207310:3(519-525)Online publication date: 1-Jul-2006
  • (2005)Biclustering of Expression Data Using Simulated AnnealingProceedings of the 18th IEEE Symposium on Computer-Based Medical Systems10.1109/CBMS.2005.37(383-388)Online publication date: 23-Jun-2005

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