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2017 | OriginalPaper | Buchkapitel

A Comparative Study of Feature Selection and Classification Techniques for High-Throughput DNA Methylation Data

verfasst von : Alhasan Alkuhlani, Mohammad Nassef, Ibrahim Farag

Erschienen in: Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2016

Verlag: Springer International Publishing

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Abstract

The high dimensionality of data is a common problem in classification. In this work, a small number of significant features is investigated to classify data of two sample groups. Various feature selection and classification techniques are applied in a collection of four high-throughput DNA methylation microarray data sets. Using accuracy as a performance metric, the repeated 10-fold cross-validation strategy is implemented to evaluate the different proposed techniques. Combining the Signal to Noise Ratio (SNR) and Wilcoxon rank-sum test filter methods with Support Vector Machine-Recursive Feature Elimination (SVM-RFE) as an embedded method has resulted in a perfect performance. In addition, the linear classifiers showed excellent results compared to others classifiers when applied to such data sets.

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Li, D., Xie, Z., Le Pape, M., Dye, T.: An evaluation of statistical methods for dna methylation microarray data analysis. BMC Bioinform. 16(1), 1 (2015)CrossRef

Das, P.M., Singal, R.: DNA methylation and cancer. J. Clin. Oncol. 22(22), 4632–4642 (2004)CrossRef

Zhuang, J., Widschwendter, M., Teschendorff, A.E.: A comparison of feature selection and classification methods in dna methylation studies using the illumina infinium platform. BMC Bioinform. 13(1), 59 (2012)CrossRef

Lee, C.P., Leu, Y.: A novel hybrid feature selection method for microarray data analysis. Appl. Soft Comput. 11(1), 208–213 (2011)CrossRef

Saeys, Y., Inza, I., Larranaga, P.: A review of feature selection techniques in bioinformatics. Bioinformatics 23(19), 2507–2517 (2007)CrossRef

Cai, Z., Xu, D., Zhang, Q., Zhang, J., Ngai, S.M., Shao, J.: Classification of lung cancer using ensemble-based feature selection and machine learning methods. Mol. BioSyst. 11(3), 791–800 (2015)CrossRef

Ma, Z., Teschendorff, A.E.: A variational bayes beta mixture model for feature selection in dna methylation studies. J. Bioinform. Computat. Biol. 11(04), 1350005 (2013)CrossRef

Meng, H., Murrelle, E.L., Li, G.: Identification of a small optimal subset of CpG sites as bio-markers from high-throughput DNA methylation profiles. BMC Bioinform. 9(1), 457 (2008)CrossRef

Amin, I.I., Hassanien, A.E., Kassim, S.K., Hefny, H.A.: Big DNA methylation data analysis and visualizing in a common form of breast cancer. In: Hassanien, A.E., Azar, A.T., Snasael, V., Kacprzyk, J., Abawajy, J.H. (eds.) Big Data in Complex Systems. SBD, vol. 9, pp. 375–392. Springer, Heidelberg (2015)

10.

Valavanis, I., Pilalis, E., Georgiadis, P., Kyrtopoulos, S., Chatziioannou, A.: Cancer biomarkers from genome-scale DNA methylation: Comparison of evolutionary and semantic analysis methods. Microarrays 4(4), 647–670 (2015)CrossRef

11.

Gunavathi, C., Premalatha, K.: Cuckoo search optimisation for feature selection in cancer classification: a new approach. Int. J. Data Min. Bioinform. 13(3), 248–265 (2015)CrossRef

12.

Guyon, I., Weston, J., Barnhill, S., Vapnik, V.: Gene selection for cancer classification using support vector machines. Mach. Learn. 46(1–3), 389–422 (2002)MATHCrossRef

13.

Zhou, X., Tuck, D.P.: MSVM-RFE: extensions of SVM-RFE for multiclass gene selection on DNA microarray data. Bioinformatics 23(9), 1106–1114 (2007)CrossRef

14.

Bibikova, M., Le, J., Barnes, B., Saedinia-Melnyk, S., Zhou, L., Shen, R., Gunderson, K.L.: Genome-wide dna methylation profiling using infinium\(\textregistered \) assay. Epigenomics 1(1), 177–200 (2009)CrossRef

15.

Bibikova, M., Barnes, B., Tsan, C., Ho, V., Klotzle, B., Le, J.M., Delano, D., Zhang, L., Schroth, G.P., Gunderson, K.L., et al.: High density dna methylation array with single CpG site resolution. Genomics 98(4), 288–295 (2011)CrossRef

16.

Lipworth, L., Morgans, A.K., Edwards, T.L., Barocas, D.A., Chang, S.S., Herrell, S.D., Penson, D.F., Resnick, M.J., Smith, J.A., Clark, P.E.: Renal cell cancer histological subtype distribution differs by race and sex. BJU Int. 117(2), 260–265 (2016)CrossRef

17.

Liu, Y., Aryee, M.J., Padyukov, L., Fallin, M.D., Hesselberg, E., Runarsson, A., Reinius, L., Acevedo, N., Taub, M., Ronninger, M., et al.: Epigenome-wide association data implicate DNA methylation as an intermediary of genetic risk in rheumatoid arthritis. Nat. Biotechnol. 31(2), 142–147 (2013)CrossRef

18.

Teschendorff, A.E., Menon, U., Gentry-Maharaj, A., Ramus, S.J., Weisenberger, D.J., Shen, H., Campan, M., Noushmehr, H., Bell, C.G., Maxwell, A.P., et al.: Age-dependent dna methylation of genes that are suppressed in stem cells is a hallmark of cancer. Genome Res. 20(4), 440–446 (2010)CrossRef

19.

Dedeurwaerder, S., Defrance, M., Bizet, M., Calonne, E., Bontempi, G., Fuks, F.: A comprehensive overview of infinium humanmethylation450 data processing. Briefings Bioinform. 15(6), 929–941 (2013)CrossRef

20.

Chen, Y.A., Lemire, M., Choufani, S., Butcher, D.T., Grafodatskaya, D., Zanke, B.W., Gallinger, S., Hudson, T.J., Weksberg, R.: Discovery of cross-reactive probes and polymorphic CpGs in the illumina infinium humanmethylation450 microarray. Epigenetics 8(2), 203–209 (2013)CrossRef

21.

Zhang, Q., Wu, H., Zheng, H.: Aberrantly methylated CpG island detection in colon cancer. J. Proteomics Bioinform. 2015 (2015)

22.

Romanski, P., Kotthoff, L.: Fselector: Selecting attributes (2013). https://cran.r-project.org/web/packages/FSelector/. R package version 0.19

23.

Strobl, C., Boulesteix, A.L., Zeileis, A., Hothorn, T.: Bias in random forest variable importance measures: Illustrations, sources and a solution. BMC Bioinform. 8(1), 1 (2007)CrossRef

24.

Liang, J.D., Ping, X.O., Tseng, Y.J., Huang, G.T., Lai, F., Yang, P.M.: Recurrence predictive models for patients with hepatocellular carcinoma after radiofrequency ablation using support vector machines with feature selection methods. Comput. Methods Programs Biomed. 117(3), 425–434 (2014)CrossRef

25.

Boser, B.E., Guyon, I.M., Vapnik, V.N.: A training algorithm for optimal margin classifiers. In: Proceedings of the Fifth Annual Workshop on Computational Learning Theory, pp. 144–152. ACM (1992)

26.

Cover, T.M., Hart, P.E.: Nearest neighbor pattern classification. IEEE Trans. Inf. Theory 13(1), 21–27 (1967)MATHCrossRef

27.

Keller, A.D., Schummer, M., Hood, L., Ruzzo, W.L.: Bayesian classification of DNA array expression data. Technical Report UW-CSE-2000-08-01 (2000)

28.

Huerta, E.B., Duval, B., Hao, J.K.: A hybrid LDA and genetic algorithm for gene selection and classification of microarray data. Neurocomputing 73(13), 2375–2383 (2010)CrossRef

29.

Kuncheva, L.I.: A stability index for feature selection. In: Artificial Intelligence and Applications, pp. 421–427 (2007)

Titel: A Comparative Study of Feature Selection and Classification Techniques for High-Throughput DNA Methylation Data
verfasst von: Alhasan Alkuhlani
Mohammad Nassef
Ibrahim Farag
Verlag: Springer International Publishing
Buch: Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2016
Print ISBN: 978-3-319-48307-8

Electronic ISBN: 978-3-319-48308-5

Copyright-Jahr: 2017
DOI: https://doi.org/10.1007/978-3-319-48308-5_76

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