Characterization of 1H NMR spectroscopic data and the generation of synthetic validation sets

Bioinformatics

Volumn 25, Issue 22, 2009, Pages 2992-3000

  Anderson, Paul E ^a   Raymer, Michael L ^a   Kelly, Benjamin J ^a   Reo, Nicholas V ^b   Delraso, Nicholas J ^c   Doom, Travis E ^a  

^a Department of Computer Science and Engineering ^*  (United States)

^b WRIGHT STATE UNIVERSITY   (United States)

^c AIR FORCE RESEARCH LABORATORY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM; ARTICLE; DATA BASE; DATA MINING; INFORMATION PROCESSING; METABOLOMICS; PRIORITY JOURNAL; PROTON NUCLEAR MAGNETIC RESONANCE; STATISTICAL MODEL; VALIDATION PROCESS; BIOLOGY; METHODOLOGY; NUCLEAR MAGNETIC RESONANCE; PROTEIN DATABASE; SEQUENCE ANALYSIS;

ALGORITHMS; COMPUTATIONAL BIOLOGY; DATABASES, PROTEIN; METABOLOMICS; NUCLEAR MAGNETIC RESONANCE, BIOMOLECULAR; SEQUENCE ANALYSIS, PROTEIN;

EID: 70449408035     PISSN: 13674803     EISSN: 14602059     Source Type: Journal    
DOI: 10.1093/bioinformatics/btp540     Document Type: Article

References (41)

1. Anderson,P.E. et al. (2009) Nuclear magnetic resonance synthetic validation sets. Available from: http://birg.cs.wright.edu/ nmr_synthetic_data_sets
2. Azmi,J. et al. (2005) Chemometric analysis of biofluids following toxicant induced hepatotoxicity: A metabonomic approach to distinguish the effects of 1-naphthylisothiocyanate from its products. Xenobiotica: Fate Safety Eval. Foreign Comp. Biol. Syst., 35, 839.
3. Beckonert, (2003) NMR-based metabonomic toxicity classification: hierarchical cluster analysis and k-nearest-neighbour approaches. Anal. Chim. Acta, 490, 3.
4. Beckwith-Hall,B.M. et al. (2002) NMR-based metabonomic studies on the biochemical effects of commonly used drug carrier vehicles in the rat. Chem. Res. Toxicol., 15, 1136.
5. Bezabeh,T. et al. (2009) Detecting colorectal cancer by 1H magnetic resonance spectroscopy of fecal extracts. NMR Biomed., 22, 593-600.
6. Bijlsma,S. et al. (2006) Large-scale human metabolomics studies: A strategy for data (pre-) processing and validation. Anal. Chem., 78, 567.
7. Brindle,J.T. et al. (2002) Rapid and noninvasive diagnosis of the presence and severity of coronary heart disease using 1H-NMR-based metabonomics. Nature Med., 8, 1439.
8. Bundy,J.G. et al. (2002) Earthworm species of the genus Eisenia can be phenotypically differentiated by metabolic profiling. FEBS Lett., 521, 115.
9. Cloarec,C. et al. (2005) Evaluation of the orthogonal projection on latent structure model limitations caused by chemical shift variability and improved visualization of Biomarker changes in 1H NMR spectroscopic metabonomic studies. Anal. Chem., 77, 517-526.
10. Coleman,T.F. and Li,Y.Y. (1994) On the convergence of interior-reflective Newton methods for nonlinear minimization subject to bounds. Math. Prog., 67, 189-224.
11. Coleman,T.F. and Li,Y.Y. (1996) An interior trust region approach for nonlinear minimization subject to bounds. SIAM J. Optim., 6, 418-445.
12. Davis,R.A. et al. (2007) Adaptive binning: An improved binning method for metabolomics data using the undecimated wavelet transform. Chemometr. Intell. Lab. Syst., 85, 144-154.
13. Fiehn,O. (2002) Metabolomics - the link between genotypes and phenotypes. Plant Mol. Biol., 48, 155.
14. Forshed,J. et al. (2003) Peak alignment of NMR signals by means of a genetic algorithm. Anal. Chim. Acta, 487, 189-199.
15. Forshed,J. et al. (2005) A comparison of methods for alignment of NMR peaks in the context of cluster analysis. J. Pharm. Biomed. Anal. 38, 824-832.
16. Fritsch,F.N. and Carlson,R.E. (1980) Monotone piecewise cubic interpolation. SIAM J. Numer. Anal., 17, 238-246.
17. Gavaghan,C.L. et al. (2000) An NMR-based metabonomic approach to investigate the biochemical consequences of genetic strain differences: application to the C57BL10J and Alpk:APfCD mouse. FEBS Lett., 484, 169.
18. Gerszten,R.E. and Wang,T.J. (2008) The search for new cardiovascular biomarkers. Nature, 451, 949-952.
19. Grage,H. and Akke,M. (2003). A statistical analysis of NMR spectrometer noise. J. Magn. Reson., 162, 176-188.
20. Griffin,J.L. et al. (2001) Metabolic profiling of genetic disorders: A multitissue 1H nuclear magnetic resonance spectroscopic and pattern recognition study into dystrophic tissue. Anal. Biochem., 293, 16-21.
21. Holmes,E. et al. (2000) Chemometric models for toxicity classification based on NMR spectra of biofluids. Chem. Res. Toxicol. 13, 471-478.
22. Krishnamoorthy,K. (2006) Handbook of Statistical Distributions with Applications. Boca Raton, Chapman & Hall.
23. Lange,E. et al. (2008) Critical assessment of alignment procedures for LC-MS proteomics and metabolomics measurements. BMC Bioinformatics, 9, 375.
24. Lewis,G.D. et al. (2008) Metabolite profiling of blood from individuals undergoing planned myocardial infarction reveals early markers of myocardial injury. J. Clin. Invest., 118, 3503-3512.
25. Lindon,J.C. et al. (2001) Pattern recognition methods and applications in biomedical magnetic resonance. Progr. Nucl. Magn. Reson. Spectr., 39, 1.
26. Reo,N.V. (2002) NMR-based metabolomics. Drug Chem. Toxicol., 25 375-382.
27. Robertson,D.G. (2005) Metabonomics in toxicology: A review. Toxicol. Sci., 85, 809-822.
28. Shockcor,J.P. and Holmes,E. (2002) Metabonomic applications in toxicity screening and disease diagnosis. Curr. Topics Med. Chem., 2, 35.
29. Spearman,C. (1904) The proof and measurement of association between two things. Amer. J. Psychol., 15, 72-101.
30. Stevens,M.A. (1974) EDF statistics for goodness of fit and some comparisons. J. Amer. Statist. Assoc., 69, 730-737.
31. Stevens,M.A. (1976) Asymptotic results for goodness-of-fit statistics with unknown parameters. Ann. Statist., 4, 357-369.
32. Stevens,M.A. (1977) Goodness of fit for the extreme value distribution. Biometrika, 64, 583-588.
33. Stevens,M.A. (1979) Tests of fit for the logistic distribution based on the empirical distribution function. Biometrika, 66, 591-595.
34. Stoyanova,R. and Brown,T.R. (2002) NMR spectral quantitation by principal component analysis. J. Magn. Reson., 154, 163-175.
35. Szopa,J. et al. (2001) Identification and quantification of catecholamines in potato plants (Solanum tuberosum) by GC-MS. Phytochemistry, 58, 315.
36. Torgrip,R.J.O. et al. (2003) Peak alignment using reduced set mapping. J. Chemometr., 17, 573-582.
37. van den Berg,R.A. et al. (2006) Centering, scaling, and transformations: Improving the biological information content of metabolomics data. BMC Genomics, 7, 142.
38. Webb-Robertson,B.-J.M. et al. (2005) A study of spectral integration and normalization in NMR-based metabonomic analyses. J. Pharm. Biomed. Anal., 39, 830-836.
39. Wilson,I.D. et al. (2005) HPLC-MS-based methods for the study of metabonomics. J. Chromatogr. B, 817, 67.
40. Wong,J.W. et al. (2005a) SpecAlign - processing and alignment of mass spectra datasets. Bioinformatics, 21, 2088-2090; Available at http://physchem.ox.ac.uk/~jwong/specalign/.
41. Wong,J.W. et al. (2005b) Application of fast Fourier transform cross-correlation for the alignment of large chromatographic and spectral datasets. Anal. Chem., 77, 5655-5661.