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International Joint Conference on Biomedical Engineering Systems and Technologies

BIOSTEC 2013: Biomedical Engineering Systems and Technologies pp 207–222Cite as

Optimization of Cost Sensitive Models to Improve Prediction of Molecular Functions

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 452))

Abstract

The prediction of unknown protein functions is one of the main concerns at field of computational biology. This fact is reflected specifically in the prediction of molecular functions such as catalytic and binding activities. This, along with the massive amount of information has made that tools based on machine learning techniques have increase their popularity in the last years. However, these tools are confronted to several problems associated to the treated data, one of them is the learning with large imbalance between their categories. There exist several techniques to overcomes the class imbalance, but most of them present many weakness that difficult the obtaining of reliable results. Moreover, models based on cost sensitive learning seems to be a good choice to deal with imbalance data, yet, the obtaining of a optimal cost matrix still remains an open issue. In this paper, a methodology to calculate a optimal cost matrix for models based on cost sensitive learning is proposed. The results show the superiority of this approach compared with several techniques in the state of the art regarding to class imbalance. Tests were applied to prediction of molecular functions in Embryophyta plants.

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References

  1. Aebersold, R., Mann, M., et al.: Mass spectrometry-based proteomics. Nat. 422(6928), 198–207 (2003)

    Article  Google Scholar 

  2. Allison, D.B., Cui, X., Page, G.P., Sabripour, M.: Microarray data analysis: from disarray to consolidation and consensus. Nat. Rev. Genet. 7(1), 55–65 (2006)

    Article  Google Scholar 

  3. Ashburner, M., Ball, C.A., Blake, J.A., Botstein, D., Butler, H., Cherry, J.M., Davis, A.P., Dolinski, K., Dwight, S.S., Eppig, J.T., et al.: Gene ontology: tool for the unification of biology. Nat. Genet. 25(1), 25 (2000)

    Article  Google Scholar 

  4. Basu, M.: Data Complexity in Pattern Recognition. Springer, New York (2006)

    Book  MATH  Google Scholar 

  5. Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: Smote: synthetic minority over-sampling technique. J. Artif. Intell. Res. 16, 321–357 (2002)

    MATH  Google Scholar 

  6. Dietterich, T.G.: An experimental comparison of three methods for constructing ensembles of decision trees: bagging, boosting, and randomization. Mach. Learn. 40(2), 139–157 (2000)

    Article  Google Scholar 

  7. Ding, Z.: Diversified ensemble classifiers for highly imbalanced data learning and its application in bioinformatics. Ph.D thesis, Georgia State University (2011)

    Google Scholar 

  8. Domingos, P.: Metacost: a general method for making classifiers cost-sensitive. In: Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 155–164. ACM (1999)

    Google Scholar 

  9. Elkan, C.: The foundations of cost-sensitive learning. In: International Joint Conference on Artificial Intelligence, vol. 17, pp. 973–978. Lawrence Erlbaum Associates Ltd (2001)

    Google Scholar 

  10. García-López, S., Jaramillo-Garzón, J.A., Higuita-Vásquez, J.C., Castellanos-Domínguez, C.G.: Wrapper and filter metrics for PSO-based class balance applied to protein subcellular localization. In: 2012 Biostec-Bioinformatics (2012)

    Google Scholar 

  11. Grzymala-Busse, J.W., Stefanowski, J., Wilk, S.: A comparison of two approaches to data mining from imbalanced data. J. Intell. Manuf. 16(6), 565–573 (2005)

    Article  Google Scholar 

  12. He, H., Garcia, E.A.: Learning from imbalanced data. IEEE Trans. Knowl. Data Eng. 21(9), 1263–1284 (2009)

    Article  Google Scholar 

  13. Ho, T.K., Basu, M.: Complexity measures of supervised classification problems. IEEE Trans. Pattern Anal. Mach. Intell. 24(3), 289–300 (2002)

    Article  Google Scholar 

  14. Jain, E., Bairoch, A., Duvaud, S., Phan, I., Redaschi, N., Suzek, B., Martin, M., McGarvey, P., Gasteiger, E.: Infrastructure for the life sciences: design and implementation of the uniprot website. BMC Bioinform. 10(1), 136 (2009)

    Article  Google Scholar 

  15. Jaramillo-Garzón, J.A., Gallardo-Chacón, J.J., Castellanos-Domínguez, C.G., Perera-Lluna, A.: Predictability of gene ontology slim-terms from primary structure information in embryophyta plant proteins. BMC Bioinform. 14(1), 68 (2013)

    Article  Google Scholar 

  16. Larrañaga, P., Calvo, B., Santana, R., Bielza, C., Galdiano, J., Inza, I., Lozano, J.A., Armañanzas, R., Santafé, G., Pérez, A., et al.: Machine learning in bioinformatics. Briefings Bioinform. 7(1), 86–112 (2006)

    Article  Google Scholar 

  17. Liu, X.Y., Zhou, Z.H.: The influence of class imbalance on cost-sensitive learning: an empirical study. In: 2006 Sixth International Conference on Data Mining, ICDM’06, pp. 970–974. IEEE (2006)

    Google Scholar 

  18. Liu, X.-Y., Zhou, Z.-H.: Towards cost-sensitive learning for real-world applications. In: Cao, L., Huang, J.Z., Bailey, J., Koh, Y.S., Luo, J. (eds.) PAKDD Workshops 2011. LNCS, vol. 7104, pp. 494–505. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  19. Polikar, R.: Ensemble based systems in decision making. IEEE Circuits Syst. Mag. 6(3), 21–45 (2006)

    Article  Google Scholar 

  20. Schapire, R.E.: A brief introduction to boosting. In: International Joint Conference on Artificial Intelligence, vol. 16, pp. 1401–1406. Lawrence Erlbaum Associates Ltd (1999)

    Google Scholar 

  21. Sonnenburg, S., Schweikert, G., Philips, P., Behr, J., Rätsch, G.: Accurate splice site prediction using support vector machines. BMC Bioinform. 8(Suppl 10), S7 (2007)

    Article  Google Scholar 

  22. Su, C.T., Hsiao, Y.H.: An evaluation of the robustness of MTS for imbalanced data. IEEE Trans. Knowl. Data Eng. 19(10), 1321–1332 (2007)

    Article  Google Scholar 

  23. Mohanna, E., Valian, E., Tavakoli, S.: Improved cuckoo search algorithm for global optimization. Int. J. Commun. Inf. Technol. 1(1), 31–44 (2011)

    Google Scholar 

  24. Yang, P., Xu, L., Zhou, B.B., Zhang, Z., Zomaya, A.Y.: A particle swarm based hybrid system for imbalanced medical data sampling. BMC Genomics 10(Suppl 3), S34 (2009)

    Article  Google Scholar 

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Acknowledgements

This work is partially funded by the Research office (DIMA) at the Universidad Nacional de Colombia at Manizales and the Colombian National Research Centre (COLCIENCIAS) through grant No.111952128388 and the “jovenes investigadores e innovadores - 2010 Virginia Gutierrez de Pineda” fellowship.

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Authors and Affiliations

  1. Signal Processing and Recognition Group, Universidad Nacional de Colombia, Campus la Nubia, Km 7 vía al Magdalena, Manizales, Colombia

    Sebastián García-López, Jorge Alberto Jaramillo-Garzón & German Castellanos-Dominguez

  2. Grupo de Automática y Electrónica, Instituto Tecnológico Metropolitano, Cll 54A No 30-01, Medellín, Colombia

    Jorge Alberto Jaramillo-Garzón

Authors
  1. Sebastián García-López
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  2. Jorge Alberto Jaramillo-Garzón
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  3. German Castellanos-Dominguez
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Corresponding author

Correspondence to Jorge Alberto Jaramillo-Garzón .

Editor information

Editors and Affiliations

  1. Universitat Politècnica de Catalunya, Barcelona, Spain

    Mireya Fernández-Chimeno

  2. Instituto Gulbenkian de Ciência, Oeiras, Portugal

    Pedro L. Fernandes

  3. Boston College, Chestnut Hill, Massachusetts, USA

    Sergio Alvarez

  4. University of Guelph, Guelph, Canada

    Deborah Stacey

  5. University of Vic, Vic, Spain

    Jordi Solé-Casals

  6. Technical University of Lisbon, Lisbon, Portugal

    Ana Fred

  7. New University of Lisbon, Lisboa, Portugal

    Hugo Gamboa

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García-López, S., Jaramillo-Garzón, J.A., Castellanos-Dominguez, G. (2014). Optimization of Cost Sensitive Models to Improve Prediction of Molecular Functions. In: Fernández-Chimeno, M., et al. Biomedical Engineering Systems and Technologies. BIOSTEC 2013. Communications in Computer and Information Science, vol 452. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-44485-6_15

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  • DOI: https://doi.org/10.1007/978-3-662-44485-6_15

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-44484-9

  • Online ISBN: 978-3-662-44485-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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