Using ABC Algorithm with Shrinkage Estimator to Identify Biomarkers of Ovarian Cancer from Mass Spectrometry Analysis

  • Syarifah Adilah Mohamed Yusoff
  • Rosni Abdullah
  • Ibrahim Venkat
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8073)


Biomarker discovery through mass spectrometry analysis has continuously intrigued researchers from various fields such as analytical researchers, computer scientists and mathematicians. The uniqueness of this study relies on the ability of the proteomic patterns to detect particular disease especially at the early stage. However, identification through high-throughput mass spectrometry analysis raises some challenges. Typically, it suffers from high dimensionality of data with tens of thousands attributes and high level of redundancy and noises. Hence this study will focus on two stages of mass spectrometry pipelines; firstly we propose shrinkage estimation of covariance to evaluate the discriminant characteristics among peaks of mass spectrometry data for feature extraction; secondly a sophisticated computational technique that mimic survival and natural processing which is called as Artificial Bee Colony (ABC) as feature selection is integrated with linear SVM classifier for this biomarker discovery analysis. The proposed method is tested with real-world ovarian cancer dataset to evaluate the discrimination power, accuracy, sensitivity and also specificity.


metaheuristic feature selection swarm algorithm bio-inspired algorithm classification feature extraction 


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  1. 1.
    Armananzas, R., Saeys, Y., Inza, I., Garcia-Torres, M., Bielza, C., Van de Peer, Y., Larranaga, P.: Peakbin selection in mass spectrometry data using a consensus approach with estimation of distribution algorithms. IEEE/ACM Transactions on Computational Biology and Bioinformatics 8(3), 760–774 (2011)CrossRefGoogle Scholar
  2. 2.
    Celik, M., Karaboga, D., Koylu, F.: Artificial bee colony data miner (abc-miner). pp. 96–100. IEEE (2011)Google Scholar
  3. 3.
    Celis, J.E., Gromov, P.: Proteomics in translational cancer research: toward an integrated approach. Cancer Cell 3(1), 9–15 (2003)CrossRefGoogle Scholar
  4. 4.
    Coombes, K.R., Tsavachidis, S., Morris, J.S., Baggerly, K.A., Hung, M.C., Kuerer, H.M.: Improved peak detection and quantification of mass spectrometry data acquired from surface-enhanced laser desorption and ionization by denoising spectra with the undecimated discrete wavelet transform. Proteomics 5(16), 4107–4117 (2005)CrossRefGoogle Scholar
  5. 5.
    Efron, B., Morris, C.: Data analysis using stein’s estimator and its generalizations. Journal of the American Statistical Association 70(350), 311–319 (1975)zbMATHCrossRefGoogle Scholar
  6. 6.
    He, Z., Yu, W.: Stable feature selection for biomarker discovery. arXiv preprint arXiv:1001.0887 (2010)Google Scholar
  7. 7.
    James, W., Stein, C.: Estimation with quadratic loss. In: Proceedings of the Fourth Berkeley Symposium on Mathematical Statistics and Probability, vol. 1, pp. 361–379 (1961)Google Scholar
  8. 8.
    Karaboga, D.: An idea based on honey bee swarm for numerical optimization. Techn. Rep. TR06, Erciyes Univ. Press, Erciyes (2005)Google Scholar
  9. 9.
    Karaboga, D., Ozturk, C.: A novel clustering approach: Artificial bee colony (abc) algorithm. Applied Soft Computing 11(1), 652–657 (2011)CrossRefGoogle Scholar
  10. 10.
    Ledoit, O., Wolf, M.: Improved estimation of the covariance matrix of stock returns with an application to portfolio selection. Journal of Empirical Finance 10(5), 603–621 (2003)CrossRefGoogle Scholar
  11. 11.
    Ledoit, O., Wolf, M.: A well-conditioned estimator for large-dimensional covariance matrices. Journal of Multivariate Analysis 88(2), 365–411 (2004)MathSciNetzbMATHCrossRefGoogle Scholar
  12. 12.
    Listgarten, J., Emili, A.: Statistical and computational methods for comparative proteomic profiling using liquid chromatography-tandem mass spectrometry. Molecular & Cellular Proteomics 4(4), 419–434 (2005)CrossRefGoogle Scholar
  13. 13.
    Massart, D.L., Smeyers-Verbeke, A.J.: Practical Data Handling Visual Presentation of Data by Means of Box Plots (2005)Google Scholar
  14. 14.
    Mohd Shukran, M.A., Chung, Y.Y., Yeh, W.C., Wahid, N., Ahmad Zaidi, A.M.: Artificial bee colony based data mining algorithms for classification tasks. Modern Applied Science 5(4), 217 (2011)CrossRefGoogle Scholar
  15. 15.
    Ressom, H.W., Varghese, R.S., Drake, S.K., Hortin, G.L., Abdel-Hamid, M., Loffredo, C.A., Goldman, R.: Peak selection from maldi-tof mass spectra using ant colony optimization. Bioinformatics 23(5), 619–626 (2007)CrossRefGoogle Scholar
  16. 16.
    Sanavia, T., Aiolli, F., Da San Martino, G., Bisognin, A., Di Camillo, B.: Improving biomarker list stability by integration of biological knowledge in the learning process. BMC Bioinformatics 13(suppl. 4), S22 (2012)Google Scholar
  17. 17.
    Schäfer, J., Strimmer, K., et al.: A shrinkage approach to large-scale covariance matrix estimation and implications for functional genomics. Statistical Applications in Genetics and Molecular Biology 4(1), 32 (2005)MathSciNetCrossRefGoogle Scholar
  18. 18.
    SyarifahAdilah, M., Abdullah, R., Venkat, I.: Abc algorithm as feature selection for biomarker discovery in mass spectrometry analysis. In: 2012 4th Conference on Data Mining and Optimization (DMO), pp. 67–72. IEEE (2012)Google Scholar
  19. 19.
    Yao, J., Chang, C., Salmi, M., Hung, Y., Loraine, A., Roux, S.: Genome-scale cluster analysis of replicated microarrays using shrinkage correlation coefficient. BMC Bioinformatics 9(1), 288 (2008)CrossRefGoogle Scholar
  20. 20.
    Yusoff, S.A.M., Venkat, I., Yusof, U.K., Abdullah, R.: Bio-inspired metaheuristic optimization algorithms for biomarker identification in mass spectrometry analysis. International Journal of Natural Computing Research (IJNCR) 3(2), 64–85 (2012)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Syarifah Adilah Mohamed Yusoff
    • 1
    • 2
  • Rosni Abdullah
    • 1
  • Ibrahim Venkat
    • 1
  1. 1.School of Computer SciencesUniversiti Sains MalaysiaMalaysia
  2. 2.Dept Computer Sciences and MathematicsUniversiti Teknologi MARA Pulau PinangMalaysia

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