Metabolomics, like other omics methods, produces huge datasets of biological variables, often accompanied by the necessary metadata. However, regardless of the form in which these are produced they are merely the ground substance for assisting us in answering biological questions. In this short tutorial review and position paper we seek to set out some of the elements of “best practice” in the optimal acquisition of such data, and in the means by which they may be turned into reliable knowledge. Many of these steps involve the solution of what amount to combinatorial optimization problems, and methods developed for these, especially those based on evolutionary computing, are proving valuable. This is done in terms of a “pipeline” that goes from the design of good experiments, through instrumental optimization, data storage and manipulation, the chemometric data processing methods in common use, and the necessary means of validation and cross-validation for giving conclusions that are credible and likely to be robust when applied in comparable circumstances to samples not used in their generation.
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References
F. Achard G. Vaysseix E. Barillot (2001) ArticleTitleXML, bioinformatics and data integration Bioinformatics 17 115–125 Occurrence Handle10.1093/bioinformatics/17.2.115 Occurrence Handle1:CAS:528:DC%2BD3MXisFymsbs%3D Occurrence Handle11238067
A. Aharoni C.H. Ric de Vos H.A. Verhoeven et al. (2002) ArticleTitleNontargeted metabolome analysis by use of Fourier transform ion cyclotron mass spectrometry Omics 6 217–234 Occurrence Handle10.1089/15362310260256882 Occurrence Handle1:CAS:528:DC%2BD38Xnsl2ktLY%3D Occurrence Handle12427274
J.K. Allen H.M. Davey D. Broadhurst et al. (2003) ArticleTitleHigh-throughput characterisation of yeast mutants for functional genomics using metabolic footprinting Nat. Biotechnol. 21 692–696 Occurrence Handle10.1038/nbt823 Occurrence Handle1:CAS:528:DC%2BD3sXktFSlu78%3D Occurrence Handle12740584
Allen, J.K., Davey, H.M., Broadhurst, D., Rowland, J.J., Oliver, S.G. and Kell, D.B. (2004). Discrimination of the modes of action of antifungal substances using metabolic footprinting. Appl. Environ. Micorbiol. 70, 6157–6165
T. Bäck D.B. Fogel Z. Michalewicz (Eds) (1997) Handbook of Evolutionary Computation IOPPublishing/Oxford University Press Oxford
W. Banzhaf P. Nordin R.E. Keller F.D. Francone (1998) Genetic Programming: An Introduction Morgan Kaufmann San Francisco
J.D. Barrow J. Silk (1995) The Left Hand of Creation: The Origin and Evolution of the Expanding Universe Penguin London
R. Battiti (1994) ArticleTitleUsing mutual information for selecting features in supervised neural net learning IEEE Trans. Neural Networks 5 537–550 Occurrence Handle10.1109/72.298224
E. Bauer R. Kohavi (1999) ArticleTitleAn empirical comparison of voting classification algorithms: bagging, boosting, and variants Machine Learning 36 105–139 Occurrence Handle10.1023/A:1007515423169
J.M. Bernardo A.F.M. Smith (2000) Bayesian Theory Wiley Chichester
D.A. Berry (1996) Statistics: A Bayesian Perspective Duxbury Press Belmont
C.M. Bishop (1995) Neural Networks for Pattern Recognition Clarendon Press Oxford
M. Bland (1987) An Introduction to Medical Statistics Oxford University Press Oxford
Booch, G., Raumbaugh, J. and Jacobson, I. (1999). Uni.ed Modelling Language User Guide. Addison–Wesley
A. Bradford Hill I.D. Hill (1991) Bradford Hill’s Principles of Medical Statistics, 12th edn Edward Arnold London
A. Brazma P. Hingamp J. Quackenbush et al. (2001) ArticleTitleMinimum information about a microarray experiment (MIAME)-toward standards for microarray data Nat. Genet. 29 365–371
L. Breiman (2001a) ArticleTitleRandom forests Machine Learning 45 5–32 Occurrence Handle10.1023/A:1010933404324
L. Breiman (2001b) ArticleTitleStatistical modeling: the two cultures Stat. Sci. 16 199–215 Occurrence Handle10.1214/ss/1009213726 Occurrence HandleMR1874152
L. Breiman J.H. Friedman R.A. Olshen C.J. Stone (1984) Classification and Regression Trees Wadsworth International Belmont
J.T. Brindle H. Antti E. Holmes et al. (2002) ArticleTitleRapid and noninvasive diagnosis of the presence and severity of coronary heart disease using 1H-NMR-based metabonomics Nat. Med. 8 1439–1444 Occurrence Handle10.1038/nm802 Occurrence Handle1:CAS:528:DC%2BD38XptVSgtrw%3D Occurrence Handle12447357
C. Chatfield (1995) ArticleTitleModel uncertainty, data mining and statistical inference J. R. Stat. Soc. Ser. A 158 419–466
P. Chen (1976) ArticleTitleThe entity-relationship model – toward a unified view of data ACM Trans. Database Syst. 1 9–36 Occurrence Handle10.1145/320434.320440
D. Corne M. Dorigo F. Glover (Eds) (1999) New Ideas in Optimization McGraw Hill London
M. Cornell N.W. Paton C. Hedeler et al. (2003) ArticleTitleGIMS: an integrated data storage and analysis environment for genomic and functional data Yeast 20 1291–306 Occurrence Handle10.1002/yea.1047 Occurrence Handle1:CAS:528:DC%2BD3sXpsFCltL0%3D Occurrence Handle14618567
A. Cornish-Bowden M.L. Cárdenas (2001) ArticleTitleSilent genes given voice Nature 409 571–572 Occurrence Handle10.1038/35054646 Occurrence Handle1:CAS:528:DC%2BD3MXhtVygsLs%3D Occurrence Handle11214302
P. Dasgupta P.P. Chakrabarti S.C. DeSarkar (1999) Multiobjective Heuristic Search Vieweg Braunschweig
Z.S. Davies R.J. Gilbert R.J. Merry D.B. Kell M.K. Theodorou G.W. Griffith (2000) ArticleTitleEfficient improvement of silage additives using genetic algorithms Appl. Environ. Microbiol. 66 1435–1443 Occurrence Handle10.1128/AEM.66.4.1435-1443.2000 Occurrence Handle1:CAS:528:DC%2BD3cXisVWms74%3D Occurrence Handle10742224
F. De Smet J. Mathys K. Marchal G. Thijs B. De Moor Y. Moreau (2002) ArticleTitleAdaptive quality-based clustering of gene expression profiles Bioinformatics 18 735–746 Occurrence Handle10.1093/bioinformatics/18.5.735 Occurrence Handle1:CAS:528:DC%2BD38XkvV2htro%3D Occurrence Handle12050070
Dietterich, T.G. (2000). Ensemble methods in machine learning. Multiple Classifier Systems, pp. 1–15
R.O. Duda P.E. Hart D.E. Stork (2001) Pattern Classification, 2nd ed John Wiley London
Dudoit, S., Fridlyand, J. (2002). A prediction-based resampling method for estimating the number of clusters in a dataset. Genome Biol. 3, RESEARCH0036
A.L. Duran J. Yang L. Wang L.W. Sumner (2003) ArticleTitleMetabolomics spectral formatting, alignment and conversion tools (MSFACTs) Bioinformatics 19 2283–2293 Occurrence Handle10.1093/bioinformatics/btg315 Occurrence Handle1:CAS:528:DC%2BD3sXpvVSjurY%3D Occurrence Handle14630657
B. Efron R.J. Tibshirani (1993) Introduction to the Bootstrap Chapman and Hall London
Ellis, D.I., Harrigan, G.G. and Goodacre, R. (2003). Metabolic fingerprinting with Fourier transform infrared spectroscopy in Harrigan, G.G., Goodacre, R. (Eds), Metabolic profiling: its role in biomarker discovery and gene function analysis. Kluwer, Boston. pp. 111–124
V. Estivill-Castro (2002) ArticleTitleWhy so many clustering algorithms: a position paper ACM SIGKDD Explorations Newslett. Arch. 4 65–75
B.S. Everitt (1993) Cluster Analysis Edward Arnold London
D.A. Fell (1996) Understanding the Control of Metabolism Portland Press London
A.R. Fernie (2003) ArticleTitleMetabolome characterisation in plant system analysis Funct. Plant Biol. 30 111–120 Occurrence Handle10.1071/FP02163 Occurrence Handle1:CAS:528:DC%2BD3sXitVCjtbs%3D
O. Fiehn (2001) ArticleTitleCombining genomics, metabolome analysis, and biochemical modelling to understand metabolic networks Comp. Func. Genomics. 2 155–168 Occurrence Handle10.1002/cfg.82 Occurrence Handle1:CAS:528:DC%2BD3MXlsVCntbw%3D
O. Fiehn (2002) ArticleTitleMetabolomics: the link between genotypes and phenotypes Plant Mol. Biol. 48 155–171
O. Fiehn J. Kopka P. Dormann T. Altmann R.N. Trethewey L. Willmitzer (2000) ArticleTitleMetabolite profiling for plant functional genomics Nat. Biotechnol. 18 1157–1161 Occurrence Handle10.1038/81137 Occurrence Handle1:CAS:528:DC%2BD3cXotVSmtL0%3D Occurrence Handle11062433
Fiehn, O. and Spranger, J. (2003). Use of metabolomics to discover metabolic patterns associated with human disease in Harrigan, G.G. and Goodacre, R. (Eds), Metabolic profiling: its role in biomarker discovery and gene function analysis. Kluwer Academic Publishers, Boston, pp. 199–215
O. Fiehn W. Weckwerth (2003) ArticleTitleDeciphering metabolic networks Eur. J. Biochem. 270 579–588 Occurrence Handle10.1046/j.1432-1033.2003.03427.x Occurrence Handle1:CAS:528:DC%2BD3sXhslCru7w%3D Occurrence Handle12581198
R.A. Fisher (1951) The Design of Experiments, 6th ed Oliver & Boyd Edinburgh
R.D. Fleischmann M.D. Adams O. White et al. (1995) ArticleTitleWhole-genome random sequencing and assembly of Haemophilus influenzae Rd Science 269 496–512 Occurrence Handle7542800
B. Flury H. Riedwyl (1988) Multivariate Statistics: A Practical Approach Chapman and Hall Londonc
J.A. Foster (2001) ArticleTitleEvolutionary computation Nat. Rev. Genet. 2 428–436 Occurrence Handle10.1038/35076523 Occurrence Handle1:CAS:528:DC%2BD3MXktlKqurw%3D Occurrence Handle11389459
R.J. Gilbert R. Goodacre A.M. Woodward D.B. Kell (1997) ArticleTitleGenetic programming: a novel method for the quantitative analysis of pyrolysis mass spectral data Anal. Chem. 69 4381–4389 Occurrence Handle10.1021/ac970460j Occurrence Handle1:CAS:528:DyaK2sXmt12msrk%3D
R.J. Gilbert H.E. Johnson J.J. Rowland et al. (1999) Genetic programming as an analytical tool for metabolome data W.B. Langdon R. Poli P. Nodin T. Fogarty (Eds) Late-breaking papers of EuroGP-99, Software Engineering CWI Amsterdam 23–33
R. Goodacre (2003) ArticleTitleExplanatory analysis of spectroscopic data using machine learning of simple, interpretable rules Vib. Spectrosc. 32 33–45 Occurrence Handle10.1016/S0924-2031(03)00045-6 Occurrence Handle1:CAS:528:DC%2BD3sXls1alsb4%3D
R. Goodacre D.B. Kell (1993) ArticleTitleRapid and quantitative analysis of bioprocesses using pyrolysis mass spectrometry and neural networks. Application to indole production Anal. Chim. Acta. 279 17–26 Occurrence Handle10.1016/0003-2670(93)85062-O Occurrence Handle1:CAS:528:DyaK3sXkvFemt7s%3D
R. Goodacre D.B. Kell (1996) ArticleTitlePyrolysis mass spectrometry and its applications in biotechnology Curr. Opin. Biotechnol. 7 20–28 Occurrence Handle10.1016/S0958-1669(96)80090-5 Occurrence Handle1:CAS:528:DyaK28Xhtlahsbs%3D Occurrence Handle8791308
R. Goodacre D.B. Kell (2003) Evolutionary computation for the interpretation of metabolome data G.G. Harrigan R. Goodacre (Eds) Metabolic profiling: its role in biomarker discovery and gene function analysis Kluwer Academic Publishers Boston 239–256
R. Goodacre D.B. Kell G. Bianchi (1992) ArticleTitleNeural networks and olive oil Nature 359 594 Occurrence Handle10.1038/359594a0
R. Goodacre D.B. Kell G. Bianchi (1993) ArticleTitleRapid assessment of the adulteration of virgin olive oils by other seed oils using pyrolysis mass spectrometry and artificial neural networks J. Sci. Food Agric. 63 297–307 Occurrence Handle1:CAS:528:DyaK2cXltlSnug%3D%3D
R. Goodacre S. Vaidyanathan G. Bianchi D.B. Kell (2002) ArticleTitleMetabolic profiling using direct infusion electrospray ionisation mass spectrometry for the characterisation of olive oils Analyst 127 1457–1462 Occurrence Handle10.1039/b206037j Occurrence Handle1:CAS:528:DC%2BD38Xot1Kns70%3D Occurrence Handle12475034
R. Goodacre S. Vaidyanathan W.B. Dunn G.G. Harrigan D.B. Kell (2004) ArticleTitleMetabolomics by numbers: acquiring and understanding global metabolite data Trends Biotechnol. 22 245–252 Occurrence Handle10.1016/j.tibtech.2004.03.007 Occurrence Handle1:CAS:528:DC%2BD2cXjtl2lt7k%3D Occurrence Handle15109811
M. Halkidi Y. Batistakis M. Vazirgiannis (2001) ArticleTitleOn clustering validation techniques J. Intell. Inf. Syst. 17 107–145 Occurrence Handle10.1023/A:1012801612483
Handl, J. and Knowles, J. (2004) Evolutionary Multiobjective Clustering. PPSN VIII, LNCS 3242, 1081-1091 (see http://dbk.ch.umist.ac.uk/Papers/HandlKnowlesPPSN-webversion.pdf)
N. Hardy H. Fuell (2003) Databases, data modeling and schemas: database development in metabolomics G.G. Harrigan R. Goodacre (Eds) Metabolic profiling: its role in biomarker discovery and gene function analysis Kluwer Academic Publishers Boston 277–291
G.G. Harrigan R. Goodacre (Eds) (2003) Metabolic Profiling: Its Role in Biomarker Discovery and Gene Function Analysis Kluwer Academic Publishers Boston
T. Hastie R. Tibshirani J. Friedman (2001) The Elements of Statistical Learning: Data Mining, Inference and Prediction Springer-Verlag Berlin
R. Heinrich S. Schuster (1996) The Regulation of Cellular Systems Chapman & Hall New York
C.R. Hicks K.V. Turner SuffixJr (1999) Fundamental Concepts in the Design of Experiments, 5th edn Oxford University Press Oxford
J.H. Hofmeyr A. Cornish-Bowden (1996) ArticleTitleCo-response analysis: a new experimental strategy for metabolic control analysis J. Theor. Biol. 182 371–380 Occurrence Handle10.1006/jtbi.1996.0176 Occurrence Handle1:CAS:528:DyaK28XntVaksL8%3D Occurrence Handle8944170
M. Hucka A. Finney H.M. Sauro et al. (2003) ArticleTitleThe systems biology markup language (SBML): a medium for representation and exchange of biochemical network models Bioinformatics 19 524–531 Occurrence Handle1:CAS:528:DC%2BD3sXit1ygu78%3D Occurrence Handle12611808
A.K. Jain R.C. Dubes (1988) Algorithms for Clustering Data Prentice Hall Englewood Cliffs, NJ
A.K. Jain M.N. Murty P.J. Flynn (1999) ArticleTitleData clustering: a review ACM Comput. Surveys 31 264–323 Occurrence Handle10.1145/331499.331504
H. Jenkins N. Hardy M. Beckmann J. Draper A.R. Smith J. Taylor et al. (2004) ArticleTitleA proposed framework for the description of plant metabolomics experiments and their results. Nature Biotechnol 22 1601–1606 Occurrence Handle10.1038/nbt1041 Occurrence Handle1:CAS:528:DC%2BD2cXhtVCku77M
H.E. Johnson D. Broadhurst R. Goodacre A.R. Smith (2003) ArticleTitleMetabolic fingerprinting of salt-stressed tomatoes Phytochemistry 62 919–928 Occurrence Handle10.1016/S0031-9422(02)00722-7 Occurrence Handle1:CAS:528:DC%2BD3sXhtVelt70%3D Occurrence Handle12590119
H.E. Johnson R.J. Gilbert M.K. Winson et al. (2000) ArticleTitleExplanatory analysis of the metabolome using genetic programming of simple, interpretable rules Genet. Progr. Evolvable Machines 1 243–258 Occurrence Handle10.1023/A:1010014314078
I.T. Jolliffe (1986) Principal Component Analysis Springer-Verlag New York
A. Jones E. Hunt J.M. Wastling A. Pizarro C.J. Stoeckert SuffixJr (2004) ArticleTitleAn object model and database for functional genomics Bioinformatics 20 1583–1590 Occurrence Handle10.1093/bioinformatics/bth130 Occurrence Handle1:CAS:528:DC%2BD2cXltlOqtbo%3D Occurrence Handle15145818
N.N. Kaderbhai D.I. Broadhurst D.I. Ellis R. Goodacre D.B. Kell (2003) ArticleTitleFunctional genomics via metabolic footprinting: monitoring metabolite secretion by Escherichia coli tryptophan metabolism mutants using FT-IR and direct injection electrospray mass spectrometry Comp. Funct. Genom. 4 376–391 Occurrence Handle10.1002/cfg.302 Occurrence Handle1:CAS:528:DC%2BD3sXnsV2jt74%3D
D.B. Kell (2002) ArticleTitleMetabolomics and machine learning: explanatory analysis of complex metabolome data using genetic programming to produce simple, robust rules Mol. Biol. Rep. 29 237–241 Occurrence Handle10.1023/A:1020342216314 Occurrence Handle1:CAS:528:DC%2BD38Xnt1agur8%3D Occurrence Handle12241064
D.B. Kell (2004) ArticleTitleMetabolomics and systems biology: making sense of the soup Curr. Opin. Microbiol. 7 296–307 Occurrence Handle10.1016/j.mib.2004.04.012 Occurrence Handle1:CAS:528:DC%2BD2cXkvV2nsLo%3D Occurrence Handle15196499
D.B. Kell R.M. Darby J. Draper (2001) ArticleTitleGenomic computing: explanatory analysis of plant expression profiling data using machine learning Plant Physiol. 126 943–951 Occurrence Handle10.1104/pp.126.3.943 Occurrence Handle1:CAS:528:DC%2BD3MXlsVartLc%3D Occurrence Handle11457944
D.B. Kell R.D. King (2000) ArticleTitleOn the optimization of classes for the assignment of unidentified reading frames in functional genomics programmes: the need for machine learning Trends Biotechnol. 18 93–98 Occurrence Handle10.1016/S0167-7799(99)01407-9 Occurrence Handle1:CAS:528:DC%2BD3cXht1Krs78%3D Occurrence Handle10675895
D.B. Kell S.G. Oliver (2004) ArticleTitleHere is the evidence, now what is the hypothesis? The complementary roles of inductive and hypothesis-driven science in the post-genomic era Bioessays 26 99–105 Occurrence Handle10.1002/bies.10385 Occurrence Handle14696046
D.B. Kell H.V. Westerhoff (1986) ArticleTitleMetabolic control theory – its role in microbiology and biotechnology FEMS Microbiol. Rev. 39 305–320 Occurrence Handle10.1016/0378-1097(86)90020-0 Occurrence Handle1:CAS:528:DyaL28XmtVOms70%3D
T. Kohonen (1989) Self-Organization and Associative Memory Springer-Verlag Berlin
F. Kose W. Weckwerth T. Linke O. Fiehn (2001) ArticleTitleVisualizing plant metabolomic correlation networks using clique-metabolite matrices Bioinformatics 17 1198–1208 Occurrence Handle10.1093/bioinformatics/17.12.1198 Occurrence Handle1:CAS:528:DC%2BD38XmtVCltw%3D%3D Occurrence Handle11751228
J.R. Koza (1992) Genetic Programming: On the Programming of Computers by Means of Natural Selection MIT Press Cambridge
J.R. Koza (1994) Genetic Programming II: Automatic Discovery of Reusable Programs MIT Press Cambridge
J.R. Koza F.H. Bennett M.A. Keane D. Andre (1999) Genetic Programming III: Darwinian Invention and Problem Solving Morgan Kaufmann San Francisco
J.R. Koza M.A. Keane M.J. Streeter W. Mydlowec J. Yu G. Lanza (2003) Genetic Programming: Routine Human-Competitive Machine Intelligence Kluwer New York
M.A. Kramer (1991) ArticleTitleNonlinear principal components analysis using auto-associative neural networks AIChE J 37 233–243 Occurrence Handle10.1002/aic.690370209 Occurrence Handle1:CAS:528:DyaK3MXht1Ghsbs%3D
W.B. Langdon (1998) Genetic Programming and Data Structures: Genetic Programming + Data Structures = Automatic Programming Kluwer Boston
W.B. Langdon R. Poli (2002) Foundations of Genetic Programming Springer-Verlag Berlin
E.M. Lenz J. Bright I.D. Wilson S.R. Morgan A.F.P. Nash (2003) ArticleTitleA 1H-NMR-based metabonomic study of urine and plasma samples obtained from healthy human subjects J. Pharm. Biomed. Anal. 33 1103–1115 Occurrence Handle10.1016/S0731-7085(03)00410-2 Occurrence Handle1:CAS:528:DC%2BD3sXptlymtb0%3D Occurrence Handle14656601
T. Leonard J.S.J. Hsu (1999) Bayesian Methods: An Analysis for Statisticians and Interdisciplinary Researchers Cambridge University Press Cambridge
Li, X.J., Brazhnik, O., Kamal, A., et al. (2003). Databases and visualization for metabolomics in Harrigan, G.G. and Goodacre, R. (Eds), Metabolic pro.ling: its role in biomarker discovery and gene function analysis. Kluwer Academic Publishers.
J.C. Lindon E. Holmes J.K. Nicholson (2003) ArticleTitleSo whats the deal with metabonomics? Metabonomics measures the fingerprint of biochemical perturbations caused by disease, drugs, and toxins Anal. Chem. 75 384A–391A Occurrence Handle1:CAS:528:DC%2BD3sXntValtrc%3D Occurrence Handle14632032
J.C. Lindon J.K. Nicholson E. Holmes et al. (2003b) ArticleTitleContemporary issues in toxicology the role of metabonomics in toxicology and its evaluation by the COMET project Toxicol. Appl. Pharmacol. 187 137–46 Occurrence Handle10.1016/S0041-008X(02)00079-0 Occurrence Handle1:CAS:528:DC%2BD3sXitlWnsbk%3D
J.C. Lindon J.K. Nicholson E. Holmes J.R. Everett (2000) ArticleTitleMetabonomics: metabolic processes studied by NMR spectroscopy of biofluids Concepts Magn. Reson. 12 289–320 Occurrence Handle10.1002/1099-0534(2000)12:5<289::AID-CMR3>3.0.CO;2-W Occurrence Handle1:CAS:528:DC%2BD3cXmsFOmtbg%3D
D. Livingstone (1995) Data Analysis for Chemists Oxford University Press Oxford
H. Martens T. Næs (1989) Multivariate Calibration John Wiley Chichester
P. Mendes (2002) ArticleTitleEmerging bioinformatics for the metabolome Brief Bioinform. 3 134–145 Occurrence Handle1:CAS:528:DC%2BD38Xmt1Gmt7o%3D Occurrence Handle12139433
Z. Michalewicz D.B. Fogel (2000) How to Solve It: Modern Heuristics Springer-Verlag Heidelberg
D. Michie D.J. Spiegelhalter C.C. Taylor (Eds) (1994) Machine Learning: Neural and Statistical Classification Ellis Horwood Chichester
T.M. Mitchell (1997) Machine Learning McGraw Hill New York
D.C. Montgomery (2001) Design and Analysis of Experiments, 5th edn Wiley Chichester
S.H. Muggleton (1990) ArticleTitleInductive logic programming New Gen. Comput. 8 295–318
R.H. Myers D.C. Montgomery (1995) Response Surface Methodology: Process and Product Optimization using Designed Experiments Wiley New York
J.K. Nicholson J. Connelly J.C. Lindon E. Holmes (2002) ArticleTitleMetabonomics: a platform for studying drug toxicity and gene function Nat Rev. Drug Discov. 1 153–161 Occurrence Handle10.1038/nrd728 Occurrence Handle1:CAS:528:DC%2BD38Xhs1aksbw%3D Occurrence Handle12120097
J.K. Nicholson I.D. Wilson (2003) ArticleTitleUnderstanding ‘global’ systems biology: metabonomics and the continuum of metabolism Nat. Rev. Drug Disc. 2 668–676 Occurrence Handle10.1038/nrd1157 Occurrence Handle1:CAS:528:DC%2BD3sXmtVGqsbs%3D
O’Hagan, S., Dunn, W.B., Brown, M., Knowles, J.D., Kell, D.B. (2004). Closed-loop, multiobjective optimisation of analytical instrumentation: gas-chromatography-time-of-flight mass spectrometry of the metabolomes of human serum and of yeast fermentations. Anal. Chem., In press
S.G. Oliver M.K. Winson D.B. Kell F. Baganz (1998) ArticleTitleSystematic functional analysis of the yeast genome Trends Biotechnol. 16 373–378 Occurrence Handle10.1016/S0167-7799(98)01214-1 Occurrence Handle1:CAS:528:DyaK1cXls1Onsbc%3D Occurrence Handle9744112
S. Orchard H. Hermjakob R. Apweiler (2003) ArticleTitleThe proteomics standards initiative Proteomics 3 1374–1376 Occurrence Handle10.1002/pmic.200300496 Occurrence Handle1:CAS:528:DC%2BD3sXmtF2rt70%3D Occurrence Handle12872238
R.D.M. Page E.C. Holmes (1998) Molecular Evolution: A Phylogenetic Approach Blackwell Science Oxford
N.W. Paton S.A. Khan A. Hayes et al. (2000) ArticleTitleConceptual modelling of genomic information Bioinformatics 16 548–557 Occurrence Handle10.1093/bioinformatics/16.6.548 Occurrence Handle1:CAS:528:DC%2BD3cXnsVelsbg%3D Occurrence Handle10980152
J. Pearl (2000) Causality: Models, Reasoning and Inference Cambridge University Press Cambridge
J.R. Quinlan (1993) C4.5: Programs for Machine Learning Morgan Kaufmann San Mateo
L.M. Raamsdonk B. Teusink D. Broadhurst et al. (2001) ArticleTitleA functional genomics strategy that uses metabolome data to reveal the phenotype of silent mutations Nat. Biotechnol. 19 45–50 Occurrence Handle10.1038/83496 Occurrence Handle1:CAS:528:DC%2BD3MXjslGmsA%3D%3D Occurrence Handle11135551
M. Ramoni P. Sabastini (1998) Theory and Practice of Bayesian Belief Networks Edward Arnold London
V.J. RaywardSmith I.H. Osman C.R. Reeves G.D. Smith (Eds) (1996) Modern heuristic search methods Wiley Chichester
C.R. Reeves (Eds) (1995) Modern heuristic techniques for combinatorial problems McGraw Hill London
B.D. Ripley (1996) Pattern Recognition and Neural Networks Cambridge University Press Cambridge
U. Roessner C. Wagner J. Kopka R.N. Trethewey L. Willmitzer (2000) ArticleTitleSimultaneous analysis of metabolites in potato tuber by gas chromatography–mass spectrometry Plant J. 23 131–142 Occurrence Handle10.1046/j.1365-313x.2000.00774.x Occurrence Handle1:CAS:528:DC%2BD3cXlvFWnsrc%3D Occurrence Handle10929108
K.J. Rothman (2002) Epidemiology: An Introduction Oxford University Press Oxford
K.J. Rothman S. Greenland (1998) Modern Epidemiology, 2nd edn Lippincott, Williams & Wilkins Philadelphia
J.J. Rowland (2003) ArticleTitleModel selection methodology in supervised learning with evolutionary computation Biosystems 72 187–196 Occurrence Handle10.1016/S0303-2647(03)00143-6 Occurrence Handle1:STN:280:DC%2BD3srmsFKlug%3D%3D Occurrence Handle14642667
J.J. Schlesselman (1982) Case–Control Studies – Design, Conduct, Analysis Oxford University Press Oxford
M.B. Seasholtz B. Kowalski (1993) ArticleTitleThe parsimony principle applied to multivariate calibration Anal. Chim. Acta 277 165–177 Occurrence Handle10.1016/0003-2670(93)80430-S Occurrence Handle1:CAS:528:DyaK3sXktl2lu7c%3D
C.E. Shannon W. Weaver (1949) The Mathematical Theory of Communication University of Illinois Press Urbana
K.S. Solanky N.J.C. Bailey B.M. Beckwith-Hall et al. (2003) ArticleTitleApplication of biofluid 1H nuclear magnetic resonance-based metabonomic techniques for the analysis of the biochemical effects of dietary isflavones on human plasma profile Anal. Biochem 323 197–204 Occurrence Handle10.1016/j.ab.2003.08.028 Occurrence Handle1:CAS:528:DC%2BD3sXptl2qtLc%3D Occurrence Handle14656525
R. Steuer J. Kurths O. Fiehn W. Weckwerth (2003) ArticleTitleObserving and interpreting correlations in metabolomic networks Bioinformatics 19 1019–1026 Occurrence Handle10.1093/bioinformatics/btg120 Occurrence Handle1:CAS:528:DC%2BD3sXktVCgs7g%3D Occurrence Handle12761066
L.W. Sumner P. Mendes R.A. Dixon (2003) ArticleTitlePlant metabolomics: large-scale phytochemistry in the functional genomics era Phytochemistry 62 817–836 Occurrence Handle10.1016/S0031-9422(02)00708-2 Occurrence Handle1:CAS:528:DC%2BD3sXhtVeltr0%3D Occurrence Handle12590110
C.F. Taylor N.W. Paton K.L. Garwood et al. (2003) ArticleTitleA systematic approach to modelling capturing and disseminating proteomics experimental data Nat. Biotechnol 21 247–254 Occurrence Handle10.1038/nbt0303-247 Occurrence Handle1:CAS:528:DC%2BD3sXhsFajsr0%3D Occurrence Handle12610571
J. Taylor R.D. King T. Altmann O. Fiehn (2002) ArticleTitleApplication of metabolomics to plant genotype discrimination using statistics and machine learning Bioinformatics 18 IssueIDSuppl 2 S241–S248
R. Tibshirani G. Walther T. Hastie (2001) ArticleTitleEstimating the number of clusters in a data set via the gap statistic J Roy Stat Soc B 63 411–423 Occurrence Handle10.1111/1467-9868.00293
E. Urbanczyk-Wochniak A. Luedemann J. Kopka et al. (2003) ArticleTitleParallel analysis of transcript and metabolic profiles: a new approach in systems biology EMBO Rep 4 989–993 Occurrence Handle10.1038/sj.embor.embor944 Occurrence Handle1:CAS:528:DC%2BD3sXnslaqt7g%3D Occurrence Handle12973302
S. Vaidyanathan D.I. Broadhurst D.B. Kell R. Goodacre (2003) ArticleTitleExplanatory optimisation of protein mass spectrometry via genetic search Anal. Chem 75 6679–6686 Occurrence Handle10.1021/ac034669a Occurrence Handle1:CAS:528:DC%2BD3sXotlOks7g%3D Occurrence Handle14640745
Vaidyanathan, S., Kell, D.B. and Goodacre, R. (2004). Selective detection of proteins in mixtures using electrospray ionization mass spectrometry: influence of instrumental settings and implications for proteomics. Anal. Chem., 76, 5024–5032
S.H. Weiss C.A. Kulikowski (1991) Computer Systems that Learn: Classification and Prediction Methods from Statistics, Neural networks, Machine Learning, and Expert Systems Morgan Kaufmann Publishers San Mateo, CA
D. Weuster-Botz C. Wandrey (1995) ArticleTitleMedium optimization by genetic algorithm for continuous production of formate dehydrogenase Proc. Biochem 30 563–571 Occurrence Handle10.1016/0032-9592(94)00036-H Occurrence Handle1:CAS:528:DyaK2MXmsFyqtLg%3D
I.D. Wilson U.A. Brinkman (2003) ArticleTitleHyphenation and hypernation the practice and prospects of multiple hyphenation J. Chromatogr. A 1000 325–356 Occurrence Handle10.1016/S0021-9673(03)00504-1 Occurrence Handle1:CAS:528:DC%2BD3sXktFGgs7g%3D Occurrence Handle12877178
A.M. Woodward J.J. Rowland D.B. Kell (2004) ArticleTitleFast automatic registration of images using the phase of a complex wavelet transform: application to proteome gels Analyst 129 542–552 Occurrence Handle10.1039/b403134b Occurrence Handle1:CAS:528:DC%2BD2cXktVOru7g%3D Occurrence Handle15152333
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Brown, M., Dunn, W.B., Ellis, D.I. et al. A metabolome pipeline: from concept to data to knowledge. Metabolomics 1, 39–51 (2005). https://doi.org/10.1007/s11306-005-1106-4
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DOI: https://doi.org/10.1007/s11306-005-1106-4