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Computational and Mathematical Methods in Medicine
Volume 2016, Article ID 6169249, 6 pages
Research Article

A Mass Spectrometric Analysis Method Based on PPCA and SVM for Early Detection of Ovarian Cancer

1College of Electrical Engineering and Instrumentation, Jilin University, Changchun 130061, China
2First Hospital, Jilin University, Changchun 130021, China

Received 21 April 2016; Revised 17 June 2016; Accepted 13 July 2016

Academic Editor: Erik Cuevas

Copyright © 2016 Jiang Wu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. American Cancer Society, Cancer Facts and Figures 2015, American Cancer Society, Atlanta, Ga, USA, 2015.
  2. U. Menon and I. Jacobs, “Ovarian cancer screening in the general population,” Ultrasound in Obstetrics & Gynecology, vol. 26, p. 243, 2012. View at Google Scholar
  3. E. F. Petricoin, A. M. Ardekani, B. A. Hitt, and P. J. Levine, “Use of proteomic patterns in serum to identify ovarian cancer,” Physics, vol. 359, pp. 572–577, 2002. View at Google Scholar
  4. L. H. Cazares, B.-L. Adam, M. D. Ward et al., “Normal, benign, preneoplastic, and malignant prostate cells have distinct protein expression profiles resolved by Surface Enhanced Laser Desorption/Ionization mass spectrometry,” Clinical Cancer Research, vol. 8, no. 8, pp. 2541–2552, 2002. View at Google Scholar · View at Scopus
  5. T. P. Conrads, V. A. Fusaro, S. Ross et al., “High-resolution serum proteomic features for ovarian cancer detection,” Endocrine-Related Cancer, vol. 11, no. 2, pp. 163–178, 2004. View at Publisher · View at Google Scholar · View at Scopus
  6. B.-L. Adam, Y. Qu, J. W. Davis et al., “Serum protein fingerprinting coupled with a pattern-matching algorithm distinguishes prostate cancer from benign prostate hyperplasia and healthy men,” Cancer Research, vol. 62, no. 13, pp. 3609–3614, 2002. View at Google Scholar · View at Scopus
  7. L. Li, D. M. Umbach, P. Terry, and J. A. Taylor, “Application of the GA/KNN method to SELDI proteomics data,” Bioinformatics, vol. 20, no. 10, pp. 1638–1640, 2004. View at Publisher · View at Google Scholar · View at Scopus
  8. E. F. Petricoin III, D. K. Ornstein, C. P. Paweletz et al., “Serum proteomic patterns for detection of prostate cancer,” Journal of the National Cancer Institute, vol. 94, no. 20, pp. 1576–1578, 2002. View at Publisher · View at Google Scholar · View at Scopus
  9. J. H. Oh, Y. Lotan, P. Gurnani, K. P. Rosenblatt, and J. Gao, “Prostate cancer biomarker discovery using high performance mass spectral serum profiling,” Computer Methods and Programs in Biomedicine, vol. 96, no. 1, pp. 33–41, 2009. View at Publisher · View at Google Scholar · View at Scopus
  10. M. Lamberto and M. Saitta, “Principal component analysis in fast atom bombardment-mass spectrometry of triacylglycerols in edible oils,” Journal of the American Oil Chemists' Society, vol. 72, no. 8, pp. 867–871, 1995. View at Publisher · View at Google Scholar · View at Scopus
  11. P. Johansson and M. Ringnér, “Artificial neural network for charge prediction in metabolite identification by mass spectrometry,” in Classification of Genomic and Proteomic Data Using Support Vector Machines Fundamentals of Data Mining in Genomics and Proteomics, pp. 187–202, 2007. View at Google Scholar
  12. H. Gu, Z. Pan, B. Xi, V. Asiago, B. Musselman, and D. Raftery, “Principal component directed partial least squares analysis for combining nuclear magnetic resonance and mass spectrometry data in metabolomics: application to the detection of breast cancer,” Analytica Chimica Acta, vol. 686, no. 1-2, pp. 57–63, 2011. View at Publisher · View at Google Scholar · View at Scopus
  13. E. Marchiori, C. R. Jimenez, M. West-Nielsen, and N. H. H. Heegaard, “Robust SVM-based biomarker selection with noisy mass spectrometric proteomic data,” in Applications of Evolutionary Computing, F. Rothlauf, J. Branke, S. Cagnoni et al., Eds., vol. 3907 of Lecture Notes in Computer Science, pp. 79–90, Springer, New York, NY, USA, 2006. View at Publisher · View at Google Scholar
  14. P. G. Lokhov, O. N. Kharybin, and A. I. Archakov, “Diagnosis of lung cancer based on direct-infusion electrospray mass spectrometry of blood plasma metabolites,” International Journal of Mass Spectrometry, vol. 309, pp. 200–205, 2012. View at Publisher · View at Google Scholar · View at Scopus
  15. E. Suarez, H. P. Nguyen, I. P. Ortiz et al., “Matrix-assisted laser desorption/ionization-mass spectrometry of cuticular lipid profiles can differentiate sex, age, and mating status of Anopheles gambiae mosquitoes,” Analytica Chimica Acta, vol. 706, no. 1, pp. 157–163, 2011. View at Publisher · View at Google Scholar · View at Scopus
  16. M. E. Tipping and C. M. Bishop, “Probabilistic principal component analysis,” Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol. 61, no. 3, pp. 611–622, 1999. View at Publisher · View at Google Scholar · View at MathSciNet
  17. C. C. Cheng, T. Y. Hsieh, J. S. Taur, and Y. F. Chen, “An automatic segmentation and classification framework for anti-nuclear antibody images,” BioMedical Engineering OnLine, vol. 12, supplement 1, article S5, 2013. View at Publisher · View at Google Scholar
  18. Clinical Proteomics Data Bank, 2014,
  19. N. Cristianini and J. Shawe-Taylor, Support Vector Machines and Other Kernel-Based Learning Methods, Cambridge University Press, Cambridge, Uk, 2000. View at Publisher · View at Google Scholar
  20. C. C. Chang and C. J. Lin, “LIBSVM: a library for support vector machines,” 2001, Software,