Bioanalysis and Biosensors for Bioprocess Monitoring Rapid Analysis of High-Dimensional Bioprocesses Using Multivariate Spectroscopies and Advanced Chemometrics

Created by W.Langdon from gp-bibliography.bib Revision:1.3963

@Article{shaw:2000:ABEB,
  author =       "A. D. Shaw and M. K. Winson and A. M. Woodward and 
                 A. C. McGovern and H. M. Davey and N. Kaderbhai and 
                 D. Broadhurst and R. J. Gilbert and J. Taylor and 
                 E. M. Timmins and R. Goodacre and D. B. Kell and 
                 B. K. Alsberg and J. J. Rowland",
  title =        "Bioanalysis and Biosensors for Bioprocess Monitoring
                 Rapid Analysis of High-Dimensional Bioprocesses Using
                 Multivariate Spectroscopies and Advanced Chemometrics",
  journal =      "Advances in Biochemical Engineering/Biotechnology",
  year =         "2000",
  volume =       "66",
  pages =        "83--113",
  month =        jan,
  keywords =     "genetic algorithms, genetic programming, Vibrational
                 spectroscopy, Mass spectrometry, Dielectric
                 spectroscopy, Flow Cytometry, Chemometrics",
  ISSN =         "0724-6145",
  URL =          "http://www.springerlink.com/link.asp?id=t8b4ya0bl42jnjj3",
  size =         "31 pages",
  abstract =     "There are an increasing number of instrumental methods
                 for obtaining data from biochemical processes, many of
                 which now provide information on many (indeed many
                 hundreds) of variables simultaneously. The wealth of
                 data that these methods provide, however, is useless
                 without the means to extract the required information.
                 As instruments advance, and the quantity of data
                 produced increases, the fields of bioinformatics and
                 chemometrics have consequently grown greatly in
                 importance. The chemometric methods nowadays available
                 are both powerful and dangerous, and there are many
                 issues to be considered when using statistical analyses
                 on data for which there are numerous measurements
                 (which often exceed the number of samples). It is not
                 difficult to carry out statistical analysis on
                 multivariate data in such a way that the results appear
                 much more impressive than they really are. The authors
                 present some of the methods that we have developed and
                 exploited in Aberystwyth for gathering highly
                 multivariate data from bioprocesses, and some
                 techniques of sound multivariate statistical analyses
                 (and of related methods based on neural and
                 evolutionary computing) which can ensure that the
                 results will stand up to the most rigorous scrutiny.",
  notes =        "Review, Tutorial

                 PMID: 10592527

                 variable selection.

                 p98 {"}PCA does not attempt to relate cause and effect;
                 it merely serves to highlight the larger variations in
                 the data.{"} p106 {"}principle of parsimony{"}...{"}our
                 work has shown the principle holds.{"} p107
                 {"}Statistical models are not able in general to
                 extrapolate{"}. cites \cite{koza:1995:weston}, DRASTIC
                 papers (3) p108 quote of Gould Organisms{"} influence
                 their own destiny in interesting complex and
                 comprehensible ways{"}",
}

Genetic Programming entries for Adrian D Shaw Michael K Winson Andrew M Woodward Aoife C McGovern Hazel M Davey Naheed Kaderbhai David I Broadhurst Richard J Gilbert Janet Taylor Eadaoin M Timmins Royston Goodacre Douglas B Kell Bjorn K Alsberg Jem J Rowland

Citations