Abstract
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.
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References
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)
Celik, M., Karaboga, D., Koylu, F.: Artificial bee colony data miner (abc-miner). pp. 96–100. IEEE (2011)
Celis, J.E., Gromov, P.: Proteomics in translational cancer research: toward an integrated approach. Cancer Cell 3(1), 9–15 (2003)
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)
Efron, B., Morris, C.: Data analysis using stein’s estimator and its generalizations. Journal of the American Statistical Association 70(350), 311–319 (1975)
He, Z., Yu, W.: Stable feature selection for biomarker discovery. arXiv preprint arXiv:1001.0887 (2010)
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)
Karaboga, D.: An idea based on honey bee swarm for numerical optimization. Techn. Rep. TR06, Erciyes Univ. Press, Erciyes (2005)
Karaboga, D., Ozturk, C.: A novel clustering approach: Artificial bee colony (abc) algorithm. Applied Soft Computing 11(1), 652–657 (2011)
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)
Ledoit, O., Wolf, M.: A well-conditioned estimator for large-dimensional covariance matrices. Journal of Multivariate Analysis 88(2), 365–411 (2004)
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)
Massart, D.L., Smeyers-Verbeke, A.J.: Practical Data Handling Visual Presentation of Data by Means of Box Plots (2005)
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)
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)
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)
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)
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)
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)
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)
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Mohamed Yusoff, S.A., Abdullah, R., Venkat, I. (2013). Using ABC Algorithm with Shrinkage Estimator to Identify Biomarkers of Ovarian Cancer from Mass Spectrometry Analysis. In: Pan, JS., Polycarpou, M.M., Woźniak, M., de Carvalho, A.C.P.L.F., Quintián, H., Corchado, E. (eds) Hybrid Artificial Intelligent Systems. HAIS 2013. Lecture Notes in Computer Science(), vol 8073. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40846-5_35
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DOI: https://doi.org/10.1007/978-3-642-40846-5_35
Publisher Name: Springer, Berlin, Heidelberg
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