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A Framework for the Automatic Combination and Evaluation of Gene Selection Methods

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Practical Applications of Computational Biology and Bioinformatics, 12th International Conference (PACBB2018 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 803))

Abstract

High-throughput RNA-Sequencing technologies produce large gene expression datasets whose analysis leads to a better understanding and treatment of diseases like cancer. The data’s high dimensionality poses challenges to its computational analysis, which is addressed by applying gene selection. Traditional gene selection methods are based on the data only. In turn, integrative approaches include curated biological information from external knowledge bases in the gene selection process, which improves result accuracy and computational complexity.

This paper presents a framework for comparing knowledge based and computational gene selection. Moreover, a novel integrative method of the automatic combination of both approaches is presented. Results on a cancer dataset show that simple computational methods enriched by external knowledge can compete with complex computational techniques.

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References

  1. Acharya, S., Saha, S., Nikhil, N.: Unsupervised gene selection using biological knowledge: application in sample clustering. BMC Bioinform. 18(1), 513 (2017)

    Article  Google Scholar 

  2. Ang, J.C., et al.: Supervised, unsupervised, and semi-supervised feature selection: a review on gene selection. IEEE/ACM Trans. Comput. Biol. Bioinform. 13(5), 971–989 (2016)

    Article  Google Scholar 

  3. Bellazzi, R., Zupan, B.: Towards knowledge-based gene expression data mining. J. Biomed. Inform. 40(6), 787–802 (2007)

    Article  Google Scholar 

  4. Bolón-Canedo, V., et al.: A review of microarray datasets and applied feature selection methods. Inf. Sci. 282, 111–135 (2014)

    Article  Google Scholar 

  5. Bolón-Canedo, V., Sánchez-Maroño, N., Alonso-Betanzos, A.: A review of feature selection methods on synthetic data. Knowl. Inf. Syst. 34(3), 483–519 (2013)

    Article  Google Scholar 

  6. Consortium, G.O., et al.: Expansion of the Gene Ontology knowledgebase and resources. Nucleic Acids Res. 45(D1), D331–D338 (2017)

    Google Scholar 

  7. Dash, M., Liu, H.: Feature selection for classification. Intell. Data Anal. 1(3), 131–156 (1997)

    Article  Google Scholar 

  8. Díaz-Uriarte, R., De Andres, S.A.: Gene selection and classification of microarray data using random forest. BMC Bioinform. 7(1), 3 (2006)

    Article  Google Scholar 

  9. Ding, C., Peng, H.: Minimum redundancy feature selection from microarray gene expression data. J. Bioinform. Comput. Biol. 3(02), 185–205 (2005)

    Article  Google Scholar 

  10. Durbin, B.P., et al.: A variance-stabilizing transformation for gene-expression microarray data. Bioinformatics 18(suppl. 1), S105–S110 (2002)

    Article  Google Scholar 

  11. Fang, O.H., Mustapha, N., Sulaiman, M.N.: An integrative gene selection with association analysis for microarray data classification. Intell. Data Anal. 18(4), 739–758 (2014)

    Google Scholar 

  12. Grossman, R.L., et al.: Toward a shared vision for cancer genomic data. N. Engl. J. Med. 375(12), 1109–1112 (2016)

    Article  Google Scholar 

  13. Guyon, I., et al.: Gene selection for cancer classification using support vector machines. Mach. Learn. 46(1–3), 389–422 (2002)

    Article  Google Scholar 

  14. Hall, M., et al.: The WEKA data mining software: an update. SIGKDD Explor. 11(1), 10–18 (2009)

    Article  Google Scholar 

  15. Kanehisa, M., Goto, S.: KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 28(1), 27–30 (2000)

    Article  Google Scholar 

  16. Kononenko, I.: Estimating attributes: analysis and extensions of RELIEF. In: European Conference on Machine Learning, pp. 171–182. Springer (1994)

    Google Scholar 

  17. Kukurba, K.R., Montgomery, S.B.: RNA sequencing and analysis. Cold Spring Harb. Protoc. 2015(11), 951–69 (2015)

    Article  Google Scholar 

  18. Leung, Y., Hung, Y.: A multiple-filter-multiple-wrapper approach to gene selection and microarray data classification. IEEE/ACM Trans. Comput. Biol. Bioinform. 7(1), 108–117 (2010)

    Article  Google Scholar 

  19. Liu, H., Liu, L., Zhang, H.: Ensemble gene selection by grouping for microarray data classification. J. Biomed. Inform. 43(1), 81–87 (2010)

    Article  Google Scholar 

  20. Mundra, P.A., Rajapakse, J.C.: SVM-RFE with MRMR filter for gene selection. IEEE Trans. Nanobiosci. 9(1), 31–37 (2010)

    Article  Google Scholar 

  21. Ooi, C., Tan, P.: Genetic algorithms applied to multi-class prediction for the analysis of gene expression data. Bioinformatics 19(1), 37–44 (2003)

    Article  Google Scholar 

  22. Papachristoudis, G., Diplaris, S., Mitkas, P.A.: SoFoCles: feature filtering for microarray classification based on gene ontology. J. Biomed. Inform. 43(1), 1–14 (2010)

    Article  Google Scholar 

  23. Piñero, J., et al.: DisGeNET: a discovery platform for the dynamical exploration of human diseases and their genes. Database 2015 (2015)

    Google Scholar 

  24. Qi, J., Tang, J.: Integrating gene ontology into discriminative powers of genes for feature selection in microarray data. In: Proceedings of APGV. ACM (2007)

    Google Scholar 

  25. Quanz, B., Park, M., Huan, J.: Biological pathways as features for microarray data classification. In: International Workshop on Data and Text Mining in Biomedical Informatics, pp. 5–12. ACM (2008)

    Google Scholar 

  26. Raghu, V.K., et al.: Integrated theory-and data-driven feature selection in gene expression data analysis. In: Proceedings of International Conference on Data Engineering, pp. 1525–1532. IEEE (2017)

    Google Scholar 

  27. Sharma, A., Imoto, S., Miyano, S.: A top-r feature selection algorithm for microarray gene expression data. IEEE/ACM Trans. Comput. Biol. Bioinform. 9(3), 754–764 (2012)

    Article  Google Scholar 

  28. Soh, D., et al.: Enabling more sophisticated gene expression analysis for understanding diseases and optimizing treatments. SIGKDD Explor. 9(1), 3–13 (2007)

    Article  Google Scholar 

  29. Weinstein, J.N., et al.: The cancer genome atlas pan-cancer analysis project. Nat. Genet. 45(10), 1113–1120 (2013)

    Article  Google Scholar 

  30. Yang, F., Mao, K.: Robust feature selection for microarray data based on multicriterion fusion. IEEE/ACM Trans. Comput. Biol. Bioinform. 8(4), 1080–1092 (2011)

    Article  Google Scholar 

  31. Zhao, Z., et al.: An integrative approach to identifying biologically relevant genes. In: Proceedings of SIAM International Conference Data Mining 2010, pp. 838–849. SIAM (2010)

    Google Scholar 

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Authors and Affiliations

  1. Hasso Plattner Institute, Potsdam, Germany

    Bastien Grasnick, Cindy Perscheid & Matthias Uflacker

Authors
  1. Bastien Grasnick
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  2. Cindy Perscheid
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  3. Matthias Uflacker
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Corresponding author

Correspondence to Cindy Perscheid .

Editor information

Editors and Affiliations

  1. Escuela Superior de Ingeniería Informática, Universidad de Vigo, Ourense, Spain

    Florentino Fdez-Riverola

  2. Faculty of Computing, Department of Software Engineering, Universiti Teknologi Malaysia, Johor, Malaysia

    Mohd Saberi Mohamad

  3. Department de Informática, Universidade do Minho, Braga, Portugal

    Miguel Rocha

  4. Departamento de Informática y Automática, Facultad de Ciencias, Universidad de Salamanca, Salamanca, Spain

    Juan F. De Paz

  5. Departamento de Sistemas Informáticos, Universidad de Castilla-La Mancha, Albacete, Albacete, Spain

    Pascual González

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Grasnick, B., Perscheid, C., Uflacker, M. (2019). A Framework for the Automatic Combination and Evaluation of Gene Selection Methods. In: Fdez-Riverola, F., Mohamad, M., Rocha, M., De Paz, J., González, P. (eds) Practical Applications of Computational Biology and Bioinformatics, 12th International Conference. PACBB2018 2018. Advances in Intelligent Systems and Computing, vol 803. Springer, Cham. https://doi.org/10.1007/978-3-319-98702-6_20

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