Bioanalysis and Biosensors for Bioprocess Monitoring Rapid Analysis of High-Dimensional Bioprocesses Using Multivariate Spectroscopies and Advanced Chemometrics
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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",
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title = "Bioanalysis and Biosensors for Bioprocess Monitoring
Rapid Analysis of High-Dimensional Bioprocesses Using
Multivariate Spectroscopies and Advanced Chemometrics",
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journal = "Advances in Biochemical Engineering/Biotechnology",
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year = "2000",
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volume = "66",
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pages = "83--113",
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month = jan,
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keywords = "genetic algorithms, genetic programming, Vibrational
spectroscopy, Mass spectrometry, Dielectric
spectroscopy, Flow Cytometry, Chemometrics",
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publisher = "Springer",
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ISSN = "0724-6145",
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isbn13 = "978-3-540-66052-1",
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DOI = "doi:10.1007/3-540-48773-5_3",
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size = "31 pages",
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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.",
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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
Angharad 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