A flocking based data mining algorithm for detecting outliers in cancer gene expression microarray data

Proceedings - 2012 International Conference on Information Retrieval and Knowledge Management, CAMP'12

Volumn , Issue , 2012, Pages 305-311

  Bellaachia, Abdelghani ^a   Bari, Anasse ^a  

^a The George Washington University   (United States)

Author keywords

bioinformatics; computational intelligence; gene expression; information retrieval; microarray; pattern clustering; swarm intelligence

Indexed keywords

ARTIFICIAL LIFE; BIOLOGICALLY INSPIRED ALGORITHMS; CANCER GENE EXPRESSION; COLON CANCER; DATA MINING ALGORITHM; DATA SETS; MAJOR FACTORS; MICROARRAY DATA; MICROARRAY GENE EXPRESSION DATA; NATURAL PHENOMENA; OUTLIER DETECTION ALGORITHM; OUTLIER SAMPLES; PATTERN CLUSTERING; SUB-SWARMS; SWARM INTELLIGENCE; VIRTUAL SPACES;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; BIOINFORMATICS; DATA MINING; DISEASES; EXPERIMENTS; GENE EXPRESSION; INFORMATION RETRIEVAL; KNOWLEDGE MANAGEMENT; MICROARRAYS;

STATISTICS;

EID: 84863087795     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/InfRKM.2012.6204996     Document Type: Conference Paper

References (21)

1. Maji, P.; , "Mutual Information-Based Supervised Attribute Clustering for Microarray Sample Classification," Knowledge and Data Engineering, IEEE Transactions on , vol.24, no.1, pp.127-140, Jan. 2012 doi: 10.1109/TKDE.2010.210
2. A. Bellaachia and Erhan Guven, Predicting Breast Cancer Survivability using Data Mining Techniques , Ninth Workshop on Mining Scientific and Engineering Datasets in conjunction with the Sixth SIAM International Conference on Data Mining (SDM 2006), Saturday, April 22, 2006
3. Shieh, Albert D. and Hung, Yeung Sam, (2009), Detecting Outlier Samples in Microarray Data, Statistical Applications in Genetics and Molecular Biology, 8, issue 1, number 13
4. X. Cui and T. E. Potok, Swarm Intelligence in Text Document Clustering, book chapter in "Handbook of Research on Text and Web Mining Technologies", Idea
5. X. Cui, J. Gao and T. E. Potok, A Flocking Based Algorithm for Document Clustering Analysis, Journal of System Architecture, June, 2006, ISSN: 1318-7621
6. Bellaachia, A.; Bari, A.; , "SFLOSCAN: A biologically-inspired data mining framework for community identification in dynamic social networks," Swarm Intelligence (SIS), 2011 IEEE Symposium on , vol., no., pp.1-8, 11-15 April 2011 doi: 10.1109/SIS.2011.5952580 URL: http://ieeexplore.ieee.org/stamp/ stamp.jsp?tp=&arnumber=5952580&isnumber=5952529
7. Vladimir G. Red'ko, "Artificial Life Evolutionary Models,"http://pespmc1.vub.ac.be/ALEVOMOD.html, 2005.
8. Khan, J., Wei, J., Ringner, M., Saal, L. H., Ladanyi, M., Westermann, F., Berthold, F., Schwab, M., Antonescu, C. R., Peterson, C., and Meltzer, P. S. (2001). Classication and diagnostic prediction of cancers using gene expression profiling and artificial neural networks. Nature Medicine, 7, 673-679
9. Zhu, M., and Ghodsi, A. (2006). Automatic dimensionality selection from the scree plot via the use of profile likelihood. Computational Statistics & Data Analysis, 51, 918-930
10. E. Bonabeau, M. Dorigo, and G. Theraulaz, Swarm intelligence: from natural to artificial systems, Oxford University Press, 1999.
11. Craig W. Reynolds, Flocks, Herds, and Schools: A Distributed Behavioral Model, Computer Graphics, 21(4), July 1987, pp. 25-34.]
12. T. M. Blackwell and P. J. Bentley. Dynamic search with charged swarms. In W. B. Langdon et al., editor, Genetic and Evolutionary Computation Conference, pages 19-26. Morgan Kaufmann, 2002.
13. T. M. Blackwell and P. Bentley. Don't push me! collision avoiding swarms. In Congress on Evolutionary Computation, pages 1691-1696, 2002
14. T. M. Blackwell and J. Branke. Multi-swarm optimization in dynamic environments. In G. R. Raidl, editor, Applications of Evolutionary Computing, volume 3005 of LNCS, pages 489-500. Springer, 2004.
15. Tim Blackwell. Particle swarm optimization in dynamic environments. In Evolutionary Computation in Dynamic and Uncertain Environments, pages 29-49. Springer, 2007
16. T. M. Blackwell and J. Branke. Multi-swarms, exclusion and anti-convergencein dynamic environments. IEEE transactions on Evolutionary Computation, to appear.
17. Andrea Malossini, Enrico Blanzieri and Raymond T. Ng Detecting Potential Labelling Errors in Microarrays by Data Perturbation Bioinformatics, 2006
18. Li, L., Weinberg, C. R., Darden, T. A., and Pedersen, L. G. (2001). Gene selection for sample classification based on gene expression data: study of sensitivity to choice of parametrs of the GA/KNN method. Bioinformatics, 17, 1131-1142
19. Moler, E. J., Chow M. L., and Mian, I. S. (2001). Analysis of molecular profile data using generative and discriminative methods. Physiological Genomics, 4, 109-126
20. Hubert, M., Rousseeuw, P. J., and Vanden Branden, K. (2005). ROBPCA: A new approach to robust principal component analysis. Technometrics, 47, 65-79
21. Alon,U. et al. (1999) Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotides array. Proc. Natl Acad. Sci. USA, 96, 6745-6750