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A new genetic algorithm in proteomics: Feature selection for SELDI-TOF data

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  • Reynès, Christelle
  • Sabatier, Robert
  • Molinari, Nicolas
  • Lehmann, Sylvain

Abstract

Mass spectrometry from clinical specimens is used in order to identify biomarkers in a diagnosis. Thus, a reliable method for both feature selection and classification is required. A novel method is proposed to find biomarkers in SELDI-TOF in order to perform robust classification.The feature selection is based on a new genetic algorithm. Concerning the classification, a method which takes into account the great variability on intensity by using decision stumps has been developed. Moreover, as the samples are often small, it is more appropriate to use the decision stumps simultaneously than building a complete tree. The thresholds of the decision stumps are determined in the same genetic algorithm. Finally, the method was generalized to more than two groups based on pairwise coupling. The obtained algorithm was applied on two data sets: a publicly available one containing two groups allowing a comparison with other methods from the literature and a new one containing three groups.

Suggested Citation

  • Reynès, Christelle & Sabatier, Robert & Molinari, Nicolas & Lehmann, Sylvain, 2008. "A new genetic algorithm in proteomics: Feature selection for SELDI-TOF data," Computational Statistics & Data Analysis, Elsevier, vol. 52(9), pages 4380-4394, May.
    • Handle: RePEc:eee:csdana:v:52:y:2008:i:9:p:4380-4394
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    References listed on IDEAS

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    1. Chen, Shuo & Hong, Don & Shyr, Yu, 2007. "Wavelet-based procedures for proteomic mass spectrometry data processing," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 211-220, September.
    2. Chatterjee, Sangit & Laudato, Matthew & Lynch, Lucy A., 1996. "Genetic algorithms and their statistical applications: an introduction," Computational Statistics & Data Analysis, Elsevier, vol. 22(6), pages 633-651, October.
    3. Kapetanios, George, 2007. "Variable selection in regression models using nonstandard optimisation of information criteria," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 4-15, September.
    4. Ambrogi, Federico & Lama, Nicola & Boracchi, Patrizia & Biganzoli, Elia, 2007. "Selection of artificial neural network models for survival analysis with Genetic Algorithms," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 30-42, September.
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    1. Pin Wang & Yongming Li & Bohan Chen & Xianling Hu & Jin Yan & Yu Xia & Jie Yang, 2017. "Proportional Hybrid Mechanism for Population Based Feature Selection Algorithm," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 16(05), pages 1309-1338, September.
    2. Abpeykar, Shadi & Ghatee, Mehdi & Zare, Hadi, 2019. "Ensemble decision forest of RBF networks via hybrid feature clustering approach for high-dimensional data classification," Computational Statistics & Data Analysis, Elsevier, vol. 131(C), pages 12-36.

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