ZHOU Jiarui, ZHU Zexuan, JI Zhen. A Memetic Algorithm Based Feature Weighting for Metabolomics Data Classification[J]. Chinese Journal of Electronics, 2014, 23(4): 706-711.
Citation: ZHOU Jiarui, ZHU Zexuan, JI Zhen. A Memetic Algorithm Based Feature Weighting for Metabolomics Data Classification[J]. Chinese Journal of Electronics, 2014, 23(4): 706-711.

A Memetic Algorithm Based Feature Weighting for Metabolomics Data Classification

Funds:  This work was supported in part by the National Natural Science Foundation of China Joint Fund with Guangdong, under Key Project (No.U1201256), the National Natural Science Foundation of China (No.61171125, No.61001185), in part by the NSFC-RS joint project (No.61211130120), in part by the Fok Ying-Tung Education Foundation, Guangdong Natural Science Foundation (No.S2012010009545), in part by Scientific Research Foundation for the Returned Overseas Chinese Scholars, Ministry of Education of China, in part by Science Foundation of Shenzhen City (No.JC201105170650A, No.KQC201108300045A), and in part by the Shenzhen City Foundation for Distinguished Young Scientists.
  • Received Date: 2013-03-01
  • Rev Recd Date: 2013-06-01
  • Publish Date: 2014-10-05
  • A metaheuristic chain based memetic algorithm namely MCMA is proposed for the classification of metabolomics data. MCMA regards both global evolution and local search as equivalent elemental metaheuristics, and searches with a chain of metaheuristics performed alternatively on the target problem. A hidden Markov model based scheduling mechanism is employed in MCMA for the selection of metaheuristics. By using MCMA for optimizing the linkage weight vector, a feature weighting algorithm for metabolomics data is formed to identify relevant metabolite features and reveal their exact relationships with the given physiological states. An extreme learning machine based classifier is utilized in predicting the physiological states according to the weighted metabolite features. Experimental results on real metabolomics data of clinical liver transplantation demonstrate that the proposed feature weighting and classification method obtains better performance than the other compared algorithms.
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  • M. Brown, W. Dunn, P. Dobson, et al., Mass spectrometry tools and metabolite-specific databases for molecular identification in metabolomics, Analyst, Vol.134, No.7, pp.1322-1332, 2009.
    O. Fiehn, J. Kopka, P. Dörmann, et al., Metabolite profiling for plant functional genomics, Nature Biotechnology, Vol.18, No.11, pp.1157-1161, 2000.
    M. Brown, D. Wedge, R. Goodacre, et al., Automated workflows for accurate mass-based putative metabolite identification in lc/ms-derived metabolomic datasets, Bioinformatics, Vol.27, No.8, pp.1108-1112, 2011.
    C. Smith, J. Elizabeth, G. O'Maille, et al., Xcms: Processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification, Analytical Chemistry, Vol.78, No.3, pp.779-787, 2006.
    H. Redestig and I. Costa, Detection and interpretation of metabolite-transcript coresponses using combined profiling data, Bioinformatics, Vol.27, No.13, pp.i357-i365, 2011.
    D. Brougham, G. Ivanova, M. Gottschalk, et al., Artificial neural networks for classification in metabolomic studies of whole cells using 1h nuclear magnetic resonance, Journal of Biomedicine and Biotechnology, Vol.2011, 2010.
    R. Davis, A. Charlton, S. Oehlschlager, et al., Novel feature selection method for genetic programming using metabolomic 1h NMR data, Chemometrics and Intelligent Laboratory Systems, Vol.81, No.1, pp.50-59, 2006.
    B. Kenneth, B. Lorraine and C. P'adraig, Metafind: A feature analysis tool for metabolomics data, BMC Bioinformatics, Vol.9, No.1, pp.470, 2008.
    S. Mahadevan, S. Shah, T. Marrie, et al., Analysis of metabolomic data using support vector machines, Analytical Chemistry, Vol.80, No.19, pp.7562-7570, 2008.
    Y.H. Cheng, Y.Y. Tong and X.S. Wang, Selective Bayesian classifier based on semi-supervised clustering, Chinese Journal of Electronics, Vol.21, No.1, pp.73-77, 2012.
    P. Moscato and C. Cotta, A gentle introduction to memetic algorithms, Handbook of metaheuristics, Springer, USA, pp.105-144, 2003.
    Y.S. Ong, M.H. Lim and X.S. Chen, Research frontier: Memetic computation - past, present & future, IEEE Computational Intelligence Magazine, Vol.5, No.2, pp.24-36, 2010.
    G. Huang, Q. Zhu and C. Siew, Extreme learning machine: Theory and applications, Neurocomputing, Vol.70, No.1, pp.489-501, 2006.
    G. Huang, D. Wang and Y. Lan, Extreme learning machines: A survey, International Journal of Machine Learning and Cybernetics, Vol.2, No.2, pp.107-122, 2011.
    G. Huang, H. Zhou, X. Ding, et al., Extreme learning machine for regression and multiclass classification, IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, Vol.42, No.2, pp.513-529, 2012.
    D. Wolpert and W. Macready, No free lunch theorems for optimization, IEEE Transactions on Evolutionary Computation, Vol.1, No.1, pp.67-82, 1997.
    Y. Ho and D. Pepyne, Simple explanation of the no-free-lunch theorem and its implications, Journal of Optimization Theory and Applications, Vol.115, No.3, pp.549-570, 2002.
    A. LaTorre, S. Muelas and J. Peña, A mos-based dynamic memetic differential evolution algorithm for continuous optimization: A scalability test, Soft Computing - A Fusion of Foundations, Methodologies and Applications, Vol.15, No.11, pp.2187-2199, 2011.
    D. Molina, M. Lozano, C. Garcia-Martinez, et al., Memetic algorithms for continuous optimisation based on local search chains, Evolutionary Computation, Vol.18, No.1, pp.27-63, 2010.
    A. Viterbi, Error bounds for convolutional codes and an asymptotically optimum decoding algorithm, IEEE Transactions on Information Theory, Vol.13, No.2, pp.260-269, 1967.
    D.A. Richards, M.A. Silva, N. Murphy, et al., Extracellular amino acid levels in the human liver during transplantation: A microdialysis study from donor to recipient, Amino Acids, Vol.33, No.3, pp.429-437, 2007.
    A. Qin and P. Suganthan, Self-adaptive differential evolution algorithm for numerical optimization, Proc. of IEEE Congress on Evolutionary Computation, Edinburgh, UK, pp.1785-1791, 2005.
    J. Liang, A. Qin, P. Suganthan, et al., Comprehensive learning particle swarm optimizer for global optimization of multimodal functions, IEEE Transactions on Evolutionary Computation, Vol.10, No.3, pp.281-295, 2006.
    Z. Zhan, J. Zhang, Y. Li, et al., Orthogonal learning particle swarm optimization, IEEE Transactions on Evolutionary Computation, Vol.15, No.6, pp.832-847, 2011.
    M. Powell, An efficient method for finding the minimum of a function of several variables without calculating derivatives, The Computer Journal, Vol.7, No.2, pp.155-162, 1964.
    D. Davies, W. Swann and I. Campey, Report on the development of a new direct search method of optimization, ICI Ltd., Central Instrument Laboratory Research Note, 1964.
    A. Curtis, Classification using lda, qda and logistic regression, Mach Learn, Vol.3, pp.1-23, 2005.
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