Conclusions
Microbiologists are conditioned to approach a scientific subject with a hypothesis, a protocol outlining how to proceed, and a clear idea of what they are looking for. Data mining is a relatively new concept to microbiologists, and requires a change in mind set,as a key element of this approach is to apply methods developed for other fields like statistics and bioinformatics to a search for novel facts within the accumulated data.The papers cited in Section 2 of this chapter are included here to encourage workers interested in the subject of antimicrobial resistance to approach their subject within a new paradigm. The three general methods presented in some detail, antibiotypes, multivariate analysis, and evolutionary genetics,are techniques designed to stimulate the investigator rather than present any one set approach to the subject. Interested parties are encouraged to take the first step, namely, search for colleagues with expertise in other fields to become familiar with the rich data generated by antimicrobial surveillance and other research programs from the viewpoint of their individual specialities and search for novel aspects that will shed light on a problem that will only become more critical over the coming years. Data mining is one approach that may offer novel insights into our understanding of resistance, and ultimately may result in providing potential solutions to slow the rate of resistance against organisms of medical and environmental importance.
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Keywords
- Lateral Transfer
- Intragenic Recombination
- Lateral Gene Transfer Event
- Median MICs
- Housekeeping Gene Sequence
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Agodi, A., Campanile, F., Basile, G., Viglianisi, F., and Stefani, S., 1999, Phylogenetic analysis of macrorestriction fragments as a measure of genetic relatedness in Staphylococcus aureus: The epidemiological impact of methicillin resistance. Eur. J. Epidemiol., 15, 637–642.
Box, G. E. P. and Jenkins, G. M., 1976, Time Series Analysis: Forecasting and Control, 2nd edn. Holden Day, San Francisco, CA.
Brossette, S. E., Sprague, A. P., Hardin, J. M., Waites, K. B., Jones, W. T., and Moser, S. A., 1998, Association rules and data mining in hospital infection control and public health surveillance. J. Am. Med. Inform. Assoc., 5, 373–381.
Brossette, S. E., Sprague, A. P., Jones, W. T., and Moser, S. A., 2000, A data mining system for infection control surveillance. Meth. Inf. Med., 39, 303–310.
Brown, S. M., Benneyan, J. C., Theobald, D. A., Sands, K., Hahn, M. T., Potter-Bynoe, G. A. et al., 2002, Binary cumulative sums and moving averages in nosocomial infection cluster detection. Emerg. Infect. Dis., 8, 1426–1432.
Clement, M., Posada, D., and Crandall, K. A., 2000, TCS: A computer program to estimate gene genealogies. Mol. Ecol., 9, 1657–1659.
Corso, A., Severina, E. P., Petruk, V. F., Mauriz, Y. R., and Tomasz, A., 1998, Molecular characterization of penicillin-resistant Streptococcus pneumoniae isolates causing respiratory disease in the United States. Microb. Drug Resis., 4, 325–337.
Enright, M. C. and Spratt, B. G., 1998, A multilocus sequence typing scheme for Streptococcus pneumoniae: Identification of clones associated with serious invasive disease. Microbiology, 144, 3049–3060.
Eriksson, L., Johansson, E., Kettaneh-Wold, N., and Wold, S., 2001, Multi-and Megavariate Data Analysis: Principles and Applications. Umetrics Academy, Umea, Sweden.
Felsenstein, J., 1993, PHYLIP (Phylogeny Inference Package) version 3.6a2. Distributed by the author: http://evolution.genetics.washington.edu/phylip.html, Department of Genetics, University of Washington, Seattle.
Gherardi, G., Whitney, C. G., Facklam, R. R., and Beall, B., 2000, Major related sets of antibiotic-resistant pneumococci in the United States as determined by pulsed-field gel electrophoresis and pbp1a-pbp2b-pbp2x-dhf restriction profiles. J. Infect. Dis., 181, 216–229.
Hand, D. J., 1998, Data mining: Statistics and more? Am. Statistician, 52, 112–118.
IHGSC, 2001, International Human Genome Sequencing Consortium: Initial sequencing and analysis of the human genome. Nature, 409, 860–921.
Janne, K., Pettersen, J., Lindberg, N.-O., and Lundstedt, T., 2001, Hierarchical principal component analysis (PCA) and projection to latent structure (PLS) technique on spectroscopic data as a data pretreatment for calibration. J. Chemometrics, 15, 203–213.
Kaslow, R. A. and Moser, S. A., 2000, Role of microbiology in epidemiology; before and beyond 2000. Epidemiol. Rev., 22, 131–135.
Lopez-Lazano, J. M., Monnet, D. L., Yague, A., Burgos, A., Gonzalo, N., Campillos, P. et al., 2000, Modelling and forecasting antimicrobial resistance and its dynamic relationship to antimicrobial use: A time series analysis. Int. J. Antimicrob. Agents, 14, 21–31.
MacGregor, J. F. and Kourti, T., 1995, Statistical process control of multivariate processes. Control Eng. Practice, 3, 403–414.
Maiden, M. C., Bygraves, J. A., Feil, E., Morelli, G., Russell, J. E., Urwin, R. et al., 1998, Multilocus sequence typing: A portable approach to the identification of clones within populations of pathogenic microorganisms. Proc. Natl. Acad. Sci. USA, 95, 3140–3145.
McGee, L., McDougal, L., Zhou, J., Spratt, B. G., Tenover, F. C., George, R. et al., 2001, Nomenclature of major antimicrobial-resistant clones of Streptococcus pneumoniae defined by the pneumococcal molecular epidemiology network. J. Clin. Microbiol., 39, 2565–2571.
Monnet, D. L., Lopez-Lazano, J. M., Campillos, P., Burgos, A., Yague, A., and Gonzalo, N., 2001, Making sense of antimicrobial use and resistance surveillance data: Application of ARIMA and transfer function models. Clin. Microbiol. Infect., 7, 29–36.
Morrison, D. F., 1990, Multivariate Statistical Methods, 3rd edn. McGraw-Hill, Hightstown, NJ.
Moser, S. A., Jones, W. T., and Brossette, S. E., 1999, Application of data mining to intensive care unit microbiologic data. Emerg. Infect. Dis., 5, 454–457.
NCCLS, 2000, Performance Standards for Antimicrobial Susceptibility Testing; Tenth Informational Supplement. National Committee for Clinical Laboratory Standards, Wayne, PA.
Nei, M. and Li, W. H., 1979, Mathematical model for studying genetic variation in terms of restriction endonucleases. Proc. Natl. Acad. Sci. USA, 76, 5269–5273.
Nguyen, D. V. and Rocke, D. M., 2002, Multi class cancer classification via partial least squares with gene expression profiles. Bioinformatics, 18, 1216–1226.
Peterson, L. R. and Brossette, S. E., 2002, Hunting health care-associated infections from the clinical microbiology laboratory: Passive, active and virtual surveillance. J. Clin. Microbiol., 40, 1–4.
Posada, D., 2002, Evaluation of methods for detecting recombination from DNA sequences: Empirical data. Mol. Biol. Evol., 19, 708–717.
Poupard, J., Brown, J., Gagnon, R., Stanhope, M. J., and Stewart, C., 2002, Methods for data mining from large multinational studies. Antimicrob. Agents Chemother., 46, 2409–2419.
Richter, S. S., Heilmann, K. P., Coffman, S. L., Huynh, H. K., Brueggemann, A. B., Pfaller, M. A. et al., 2002, The molecular epidemiology of penicillin-resistant Streptococcus pneumoniae in the United States, 1994–2000. Clin. Infect. Dis., 34, 330–339.
Sahm, D. F., Jones, M. E., Hickey, M. L., Diakun, D. R., Mani, S. V., and Thornsberry, C., 2000, Resistance surveillance of Streptococcus pneumoniae, Haemophilus influenzae and Moraxella catarrhalis isolated in Asia and Europe, 1997–1998. J. Antimicrob. Chemother., 45, 457–466.
SIMCA, 2000, 8.0. Umetrics AB. Umea, Sweden.
Stanhope, M. J., Lupas, A., Italia, M. J., Koretke, K. K., Volker, C., and Brown, J. R., 2001, Phylogenetic analyses do not support horizontal gene transfers from bacteria to vertebrates. Nature, 411, 940–944.
Swofford, D. L., 2002, PAUP* Version 4.0b10. Sinauer Associates, Sunderland, MA.
Templeton, A. R., 1995, A cladistic analysis of phenotypic associations with haplotypes inferred from restriction endonuclease mapping or DNA sequencing. V. Analysis of case/control sampling designs: Alzheimer’s disease and the apoprotein E locus. Genetics, 140, 403–409.
Templeton, A. R., Boerwinkle, E., and Sing, C. F., 1987, A cladistic analysis of phenotypic associations with haplotypes inferred from restriction endonuclease mapping. I. Basic theory and an analysis of alcohol dehydrogenase activity in Drosophila. Genetics, 117, 343–351.
Templeton, A. R., Crandall, K. A., and Sing, C. F., 1992, A cladistic analysis of phenotypic associations with haplotypes inferred from restriction endonuclease mapping and DNA sequence data. III. Cladogram estimation. Genetics, 132, 619–633.
Templeton, A. R. and Sing, C. F., 1993, A cladistic analysis of phenotypic associations with haplotypes inferred from restriction endonuclease mapping. IV. Nested analyses with cladogram uncertainty and recombination. Genetics, 134, 659–669.
Thornsberry, C., Ogilvie, P. T., Holley, H. P. Jr., and Sahm, D. F., 1999, Survey of susceptibilities of Streptococcus pneumoniae, Haemophilus influenzae, and Moraxella catarrhalis isolates to 26 antimicrobial agents: A prospective U.S. study. Antimicrob. Agents Chemother., 43, 2612–2623.
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Poupard, J.A., Gagnon, R.C., Stanhope, M.J. (2005). Data Mining to Discover Emerging Patterns of Antimicrobic Resistance. In: Gould, I.M., van der Meer, J.W.M. (eds) Antibiotic Policies. Springer, Boston, MA. https://doi.org/10.1007/0-387-22852-7_23
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