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Particle swarm optimization for analysis of mass spectral serum profiles

Published: 25 June 2005 Publication History

Abstract

Serum profiling using mass spectrometry is an emerging technology with a great potential to provide biomarkers for complex diseases such as cancer. However, protein profiles obtained from current mass spectrometric technologies are characterized by their high dimensionality and complex spectra with substantial level of noise. These characteristics have generated challenges in discovery of proteins and protein-profiles that distinguish cancer patients from healthy individuals. This paper proposes a novel machine learning method that combines support vector machines with particle swarm optimization for biomarker discovery. Prior to applying the proposed biomarker selection algorithm, low-level analysis methods are used for smoothing, baseline correction, normalization, and peak detection. The proposed method is applied for biomarker discovery from serum mass spectral profiles of liver cancer patients and controls.

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  • (2010)Adaptive non-dominated sorting genetic algorithms for wavelength selection of molecular hyperspectral images2010 3rd International Conference on Biomedical Engineering and Informatics10.1109/BMEI.2010.5639650(82-85)Online publication date: Oct-2010
  • (2009)A Machine Learning Approach to Mass Spectra Classification with Unsupervised Feature SelectionComputational Intelligence Methods for Bioinformatics and Biostatistics10.1007/978-3-642-02504-4_22(242-252)Online publication date: 23-Jun-2009
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cover image ACM Conferences
GECCO '05: Proceedings of the 7th annual conference on Genetic and evolutionary computation
June 2005
2272 pages
ISBN:1595930108
DOI:10.1145/1068009
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 25 June 2005

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Author Tags

  1. proteomics
  2. support vector machines
  3. swarm intelligence

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Cited By

View all
  • (2019)A Tutorial on Machine Learning for Failure Management in Optical NetworksJournal of Lightwave Technology10.1109/JLT.2019.292258637:16(4125-4139)Online publication date: 15-Aug-2019
  • (2010)Adaptive non-dominated sorting genetic algorithms for wavelength selection of molecular hyperspectral images2010 3rd International Conference on Biomedical Engineering and Informatics10.1109/BMEI.2010.5639650(82-85)Online publication date: Oct-2010
  • (2009)A Machine Learning Approach to Mass Spectra Classification with Unsupervised Feature SelectionComputational Intelligence Methods for Bioinformatics and Biostatistics10.1007/978-3-642-02504-4_22(242-252)Online publication date: 23-Jun-2009
  • (2005)Analysis of MALDI-TOF Serum Profiles for Biomarker Selection and Sample Classification2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology10.1109/CIBCB.2005.1594943(1-7)Online publication date: 2005

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