Experimental Study on Performance of Symbolic Classifier with Gene selection Methods for Multiclass Microarray Gene Expression Data

Article Sidebar

PDF
Published: Nov 1, 2022
DOI: https://doi.org/10.26483/ijarcs.v13i5.6914
Keywords:
Microarray Gene Expression, cancer classification, binary class data, high dimensional data, multiclass data, symbolic classifier.

Main Article Content

Sheela T

Abstract

Microarray is a useful technique for measuring expression data of thousands of genes simultaneously. The expression level of genes is known to contain the keys to address fundamental problems relating to the prevention and cure of diseases, biological evolution mechanisms and drug discovery. Previous research has demonstrated that this technology can be useful in the classification of cancers. Most proposed cancer classification methods work well only on binary class problems and not extensible to multi-class problems. This work is an attempt to classify high dimensional, multiclass Microarray Gene expression data using symbolic classifier.

Downloads

Download data is not yet available.

Article Details

Issue
Vol. 13 No. 5 (2022): September-October 2022
Section
Articles

COPYRIGHT

Submission of a manuscript implies: that the work described has not been published before, that it is not under consideration for publication elsewhere; that if and when the manuscript is accepted for publication, the authors agree to automatic transfer of the copyright to the publisher.

Authors who publish with this journal agree to the following terms:

  • Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
  • Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
  • Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work
  • The journal allows the author(s) to retain publishing rights without restrictions.
  • The journal allows the author(s) to hold the copyright without restrictions.

References

Ahmed, O., and Brifcani, A. (2019, April). Gene Expression Classification Based on Deep Learning. 4th Scientific International Conference Najaf (SICN) pp. 145-149, 2019.

Alomari, O.A., Khader, A.T., Al-Betar, M.A., Abualigah L.M. MRMR BA: a hybrid gene selection algorithm for cancer classification. J Theor Appl Inf Technol , 95 (12):2610–8, 2017.

Androulakis, I.P. Yang E. Almon, R.R. Analysis of time-series gene expression data: methods, challenges, and opportunities. Annu Rev Biomed Eng., 9:205–228, 2007.

Billard, L. and Diday, E.. Symbolic data analysis:Conceptual statistics and data mining. Wiley series in computational statistics. 2006.

Bock, H.H and Diday, E. Analysis of Symbolic Data. Springer Verlag, 1999.

Breiman L: Random Forests. Machine Learning. 45:5-32, 2001.

Cahyaningrum, K., and Astuti, W. Microarray Gene Expression Classification for Cancer Detection using Artificial Neural Networks and Genetic Algorithm Hybrid Intelligence. International Conference on Data Science and Its Applications (ICoDSA) (pp. 1-7). IEEE, 2020.

Christoph Bartenhagen, Hans-Ulrich Klein, Christian Ruckert, Xiaoyi Jiang and Martin Dugas. Comparative study of unsupervised dimension reduction techniques for the visualization of microarray gene expression data, BMC Bioinformatics, 11:567, 2010.

Ding, C., Peng, H. Minimum redundancy feature selection from microarray gene expression data. In:Journal Bioinformatics and Computer Biology, pp.523-529, 2003.

Diday. An introduction to symbolic data analysis and sodas software. Electro. J.Symb. Data Anal. 1-25, 2002.

Golub T.R., Slonim D.K. and Tamayo. Classification of Cancer: Class discovery and Class Prediction by Gene Expression Monitoring. Science. 286, 315-333, 1999.

Hatim Z Almarzouki. Deep-Learning-Based Cancer Profiles Classification Using Gene Expression Data Profile. Journal of Healthcare Engineering, Article ID 4715998, 13 pages, https://doi.org/10.1155/2022/4715998, 2022.

Hedjazi L. Aguilar-Martin J. Le Lann M.-V., et al. Similarity-margin based feature selection for symbolic interval data. Pattern Recognit. Lett. 32:578–585. 2011.

Hedjazi.L, Marie-Veronique Le Lann, Tatiana Kempowsky, Florence Dalenc, Joseph Aguilar-Martin, and Gilles Favre. Symbolic Data Analysis to Defy Low Signal-to-Noise Ratio in Microarray Data for Breast Cancer Prognosis J Comput Biol. 20(8): 610–620. 2013.

Inza I., Larrañaga P., Blanco R., Cerrolaza A.J. Filter versus wrapper gene selection approaches in DNA microarray domains, Artif Intell Med, 31(2):91-103, 2002.

Jeremy D. Scheff,1 Richard R. Almon,2,,3 Debra C. DuBois,2,,3 William J. Jusko,3 and Ioannis P. Androulakis A New Symbolic Representation for the Identification of Informative Genes in Replicated Microarray Experiments OMICS : A JOURNAL OF INTEGRATIVE BIOLOGY Jun 2010; 14(3): 239–248. 2010.

Lai C. M., and Huang H. P. A gene selection algorithm using simplified swarm optimization with multi-filter ensemble technique. Applied Soft Computing, 106994, 2020.

Li S. Random KNN Modeling and Variable Selection for High Dimensional Data. PhD thesis. West Virginia University, 2009.

Lin J. Keogh E. Wei L. Lonardi S. Experiencing SAX: a novel symbolic representation of time series. Data Mining Knowledge Discov. 15:107–144, 2007.

Liu Q, et al. Gene selection and classification for cancer microarray data based on machine learning and similarity measures. BMC Genomics 12(Suppl 5):S1, 2011.

Maniruzzaman M, et al. Statistical characterization and classification of colon microarray gene expression data using multiple machine learning paradigms. Comput Methods Prog Biomed;176:173–93, 2019.

E. Maguire T. Yarmush M.L. Berthiaume F. Androulakis I.P. Bioinformatics analysis of the early inflammatory response in a rat thermal injury model. BMC Bioinformatics. 2007;8:10

Othman M.S., Kumaran S. R., and Yusuf L.M. Gene Selection Using Hybrid Multi-Objective Cuckoo Search Algorithm with Evolutionary Operators for Cancer Microarray Data. IEEE Access, 8, 186348-186361, 2020.

Peng,H.,Long,F.,Ding,C. Feature selection based on mutual information : Criteria of max-dependency, max-relevance and min-redundancy. IEEE Trans Pattern Anal Machine Intell. 27(8), 1226-1238. 2005.

T.Ragunthar, S.Selvakumar. Classification of Gene Expression Data with Optimized Feature Selection. International Journal of Recent Technology and Engineering (IJRTE). ISSN: 2277-3878, Volume-8 Issue-2, July2019.

Saeys Y., Inza I., Larrañaga P. A review of feature selection techniques in bioinformatics. Bioinformatics, 23(19), 2007.

Statnikov A., Aliferis C.F., Tsamardinos I., Hardin D., Levy, S. A Comprehensive Evaluation of Multicategory Classification Methods for Microarray Gene Expression Cancer Diagnosis. Bioinformatics 21(5), 631–643, 2005.

Whitney A.W. A Direct Method of Nonparametric Measurement Selection. IEEE Trans. Comput., 20, 1100–1103. doi: 10.1109/T-C.1971.223410. 1971.

Xing E., Jordan M., Karp R. Feature selection for high-dimensional genomic microarray data. Proceedings of the 18th International Conference on Machine Learning, 2001.

Zhang X., He T., Ouyang L., Xu X., and Chen S. A Survey of Gene Selection and Classification Techniques Based on Cancer Microarray Data Analysis. IEEE 4th International Conference on Computer and Communications (ICCC) (pp. 1809-1813) IEEE, 2018.