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A Survey on Different Feature Selection Methods for Microarray Data Analysis
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 67 - Number 16 |
| Year of Publication: 2013 |
| Authors: Varuna Tyagi, Anju Mishra |
10.5120/11482-7181
|
Varuna Tyagi, Anju Mishra . A Survey on Different Feature Selection Methods for Microarray Data Analysis. International Journal of Computer Applications. 67, 16 ( April 2013), 36-40. DOI=10.5120/11482-7181
Abstract
In the field of medical science diseases diagnosis by Tissue microarrays is one of the active areas of research . There are various gene selection techniques in the literature. Gene selection provides genes subsets that are capable to describe in which category those gene are (active, hyperactive or silent). Various application areas like combinatorial chemistry, text mining, multivariate imaging, or bioinformatics are using huge data sets. The problem has been addressed of selection of a small subset of genes from broad patterns of gene expression data, recorded on DNA micro-arrays for cancer classification. Usually till now survey paper discuss various conventional & evolutionary methods of gene selection like filters, wrappers methods.
References
- Huiqing Liu, Jinyan Li, Limsoon Wong, A Comparative Study on Feature Selection and Classification Methods Using Gene Expression Profiles and Proteomic Patterns ,Laboratories for Information Technology, 21 Heng Mui Keng Terr, 119613 Singapore Genome Informatics 13: 51{60 (2002).
- Fayyad, U. and Irani, K. , Multi-interval discretization of continuous-valued attributes for classification learning, Proc. 13th International Joint Conference on Arti_cial Intelligence, 1022{1029,1993
- Golub, T. R. et al. , "Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring", Science, 286, 531{537, 1999.
- Hall, M. A. , "Correlation-based feature selection machine learning", Ph. D. Thesis, Department of Computer Science, University of Waikato, Hamilton, New Zealand, 1998.
- Li, J. and Wong, L. , Identifying good diagnostic genes or genes groups from gene expression data by using the concept of emerging patterns, Bioinformatics, 18:725{734, 2002.
- Li, J. et al. , Simple rules underlying gene expression profiles of more than six subtypes of acute lymphoblastic leukaemia (ALL) patients, Bioinformatics, in press.
- Li, J. and Wong, L. , Emerging patterns and gene expression data, Genome Informatics, 12:3{13,2001.
- Liu, H. and Setiono, R. , Chi2: Feature selection and discretization of numeric attributes, Proc. IEEE 7th International Conference on Tools with Arti_cial Intelligence, 338{391, 1995.
- Sandy R, Statistics for Business and Economics, McGrawHill, 1989.
- Cosmin Lazar, Jonatan Taminau, Stijn Meganck, David Steenhoff, Alain Coletta, Colin Molter,Virginie de Schaetzen, Robin Duque, Hugues Bersini, and Ann Nowe, A Survey on Filter Techniques for Feature Selection in Gene Expression Microarray Analysis, ieee/acm transactions on computational biology and bioinformatics, vol. 9, no. 4, july/august 2012.
- S. H. Cha, Comprehensive Survey on Distance/Similarity Measures Between Probability Density Functions, Int'l J. Math. Models and Methods in Applied Sciences, vol. 1, no. 4, pp. 300-307, 2007.
- J. Cohen, "The Earth is Round (p < . 05)," Am. Psychologist, vol. 38, pp. 997-1003, 1994.
- Y. Saeys, I. Inza, and P. Larran˜ aga, A Review of Feature Selection Techniques in Bioinformatics, Bioinformatics, vol. 23, no. 19, pp. 2507-2517, 2007.
- T. Bø and I. Jonassen, New Feature Subset Selection Procedures for Classification of Expression Profiles, Genome Biology, vol. 4, no. 4, pp. research0017. 1-research0017. 11, 2002.
- Hong Hu1, Jiuyong Li1, Hua Wang1, and Grant Daggard2, Combined Gene Selection Methods for Microarray Data Analysis.
- R. Kohavi and G. H. John, Wrappers for feature subset selection Artificial Intelligence,97(1-2):273–324, 1997.
- I. Guyon, J. Weston, S. Barnhill, and V. Vapnik. , Gene selection for cancer classification using support vector machines Machine Learning, 46(1-3):389–422, 2002.
- R. Debnath, and T. Kurita, An Evolutionary Gene Selection Method for Microarray Data Based on SVM Error Bound Theories, Neuroscience Research Institute AIST, Tsukuba, Ibaraki, 305-8568, Japan
- Daxin Jiang Chun Tang Aidong Zhang, Cluster Analysis for Gene Expression Data: A Survey, Department of Computer Science and Engineering State University of New York at Buffal.
- Wang, Haixun, Wang, Wei, Yang, Jiong and Yu, Philip S,. Clustering by Pattern Similarity in Large Data Sets, In SIGMOD 2002, Proceedings ACM SIGMOD International Conference on Management of Data, pages 394–405, 2002.
- Tavazoie, S. , Hughes, D. , Campbell, M. J. , Cho, R. J. and Church, G. M. Systematic determination of genetic network architecture. Nature Genet, pages 281–285, 1999.
- Smet, Frank De, Mathys, Janick, Marchal, Kathleen, Thijs, Gert, Moor, Bart De and Moreau, Yves. Adaptive quality-based clustering of gene expression profiles Bioinformatics, 18:735–746, 2002.
- Shamir R. and Sharan R. , Click: A clustering algorithm for gene expression analysis. In Proceedings of the 8th International Conference on Intelligent Systems for Molecular Biology (ISMB '00). AAAIPress. , 2000.
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