1887

Abstract

Surface enhanced laser desorption/ionization-time of flight mass spectrometry (SELDI-TOF MS) has been applied in large numbers of oncological studies but the microbiological field has not been extensively explored to date. This paper describes the application of SELDI-TOF MS in concert with a multi-layer perceptron artificial neural network (ANN) with a back propagation algorithm for the identification of . , the aetiological agent of gonorrhoea, is the second most common sexually transmitted disease in the UK and USA. Analysis of over 350 strains of and closely related species by SELDI-TOF MS facilitated the design of an ANN model and revealed 20 ion peak descriptors of positive, negative and secondary nature that were paramount for the identification of the pathogen. The model performed with over 96 % efficiency when based on these 20 ion peak descriptors and exhibited a sensitivity of 95.7 % and a specificity of 97.1 %, with an area under the curve value of 0.996. The technology has the potential to link several ANN models for a comprehensive rapid identification platform for clinically important pathogens.

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2005-12-01
2024-03-28
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References

  1. Agatonovic-Kustrin S., Beresford R. 2000; Basic concepts of artificial neural network (ANN) modeling and its application in pharmaceutical research. J Pharm Biomed Anal 22:717–727 [CrossRef]
    [Google Scholar]
  2. Ball G., Mian S., Holding F. & 8 other authors; 2002; An integrated approach utilizing artificial neural networks and SELDI mass spectrometry for the classification of human tumours and rapid identification of potential biomarkers. Bioinformatics 18:395–404 [CrossRef]
    [Google Scholar]
  3. Fedarko N. S. 1994; Isolation and purification of proteoglycans. EXS 70:9–35
    [Google Scholar]
  4. Fredlund H., Falk L., Jurstrand M., Unemo M. 2004; Molecular genetic methods for diagnosis and characterisation of Chlamydia trachomatis and Neisseria gonorrhoeae : impact on epidemiological surveillance and interventions. APMIS 112:771–784 [CrossRef]
    [Google Scholar]
  5. Fung E. T., Enderwick C. 2002; ProteinChip clinical proteomics: computational challenges and solutions. Biotechniques 32 Suppl:S34–S41
    [Google Scholar]
  6. Geeraerd A. H., Valdramidis V. P., Devlieghere F., Bernaert H., Debevere J., Van Impe J. F. 2004; Development of a novel approach for secondary modelling in predictive microbiology: incorporation of microbiological knowledge in black box polynomial modelling. Int J Food Microbiol 91:229–244 [CrossRef]
    [Google Scholar]
  7. Gerbase A. C., Rowley J. T., Heymann D. H., Berkley S. F., Piot P. 1998; Global prevalence and incidence estimates of selected curable STDs. Sex Transm Infect 74 Suppl 1:S12–S16
    [Google Scholar]
  8. Grus F. H., Joachim S. C., Pfeiffer N. 2003; Analysis of complex autoantibody repertoires by surface-enhanced laser desorption/ionization-time of flight mass spectrometry. Proteomics 3:957–961 [CrossRef]
    [Google Scholar]
  9. Johnson R. E., Newhall W. J., Papp J. R. & 12 other authors; 2002; Screening tests to detect Chlamydia trachomatis and Neisseria gonorrhoeae infections – 2002. MMWR Recomm Rep 51:1–38
    [Google Scholar]
  10. Khan J., Wei J. S., Ringner M. & 8 other authors; 2001; Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks. Nat Med 7:673–679 [CrossRef]
    [Google Scholar]
  11. Knapp J. S. 1988; Historical perspectives and identification of Neisseria and related species. Clin Microbiol Rev 1:415–431
    [Google Scholar]
  12. Lancashire L., Schmid O., Shah H., Ball G. 2005; Classification of bacterial species from proteomic data using combinatorial approaches incorporating artificial neural networks, cluster analysis and principal components analysis. Bioinformatics 21:2191–2199 [CrossRef]
    [Google Scholar]
  13. Mian S., Ball G., Hornbuckle J. & 8 other authors; 2003; A prototype methodology combining surface-enhanced laser desorption/ionization protein chip technology and artificial neural network algorithms to predict the chemoresponsiveness of breast cancer cell lines exposed to Paclitaxel and Doxorubicin under in vitro conditions. Proteomics 3:1725–1737 [CrossRef]
    [Google Scholar]
  14. Schwarzer G., Vach W., Schumacher M. 2000; On the misuses of artificial neural networks for prognostic and diagnostic classification in oncology. Stat Med 19:541–561 [CrossRef]
    [Google Scholar]
  15. Smith J. M., Smith N. H., O'Rourke M., Spratt B. G. 1993; How clonal are bacteria?. Proc Natl Acad Sci U S A 90:4384–4388 [CrossRef]
    [Google Scholar]
  16. Tomita Y., Tomida S., Hasegawa Y., Suzuki Y., Shirakawa T., Kobayashi T., Honda H. 2004; Artificial neural network approach for selection of susceptible single nucleotide polymorphisms and construction of prediction model on childhood allergic asthma. BMC Bioinformatics 5:120– 120 [CrossRef]
    [Google Scholar]
  17. Vazquez J. A., Berron S., O'Rourke M., Carpenter G., Feil E., Smith N. H., Spratt B. G. 1995; Interspecies recombination in nature: a meningococcus that has acquired a gonococcal PIB porin. Mol Microbiol 15:1001–1007 [CrossRef]
    [Google Scholar]
  18. Wei J. T., Zhang Z., Barnhill S. D., Madyastha K. R., Zhang H., Oesterling J. E. 1998; Understanding artificial neural networks and exploring their potential applications for the practicing urologist. Urology 52:161–172 [CrossRef]
    [Google Scholar]
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