Mass Spectrometry Metabolomic Data Handling for Biomarker Discovery - Proteomic and Metabolomic Approaches to Biomarker Discovery

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47. Lommen A. MetAlign: interface-driven, versatile

metabolomics tool for hyphenated full-scan mass

spectrometry data preprocessing. Anal Chem 2009;

64. Chong IG, Jun CH. Performance of some variable

selection methods when multicollinearity is present.

Chemom Intell Lab Syst 2005;

:103 e 12.

65. Rajalahti T, Arneberg R, Kroksveen AC, Berle M,

Myhr KM, Kvalheim OM. Discriminating variable test

and selectivity ratio plot: quantitative tools for inter-

pretation and variable (biomarker) selection in

complex spectral or chromatographic pro

3079 e 86.

48. Duran AL, Yang J, Wang LJ, Sumner LW. Metab-

olomics spectral formatting, alignment and conversion

tools (MSFACTs). Bioinformatics 2003;

:2283 e 93.

49. Luedemann A, Strassburg K, Erban A, Kopka J. Tag-

Finder for the quantitative analysis of gas chromatog-

raphy e mass spectrometry (GC-MS)-based metabolite

pro

les. Anal

:2581 e 90.

66. Steuer R. On the analysis and interpretation of correla-

tions inmetabolomic data. Brief Bioinform 2006;

Chem 2009;

:732 e 7.

50. Lei Z, Li H, Chang J, Zhao P, Sumner L. MET-IDEA

version 2.06;

ling experiments. Bioinformatics 2008;

:151 e 8.

67. Hall MA. Correlation-Based Feature Selection for Discrete

and Numeric Class Machine Learning . San Francisco:

Morgan Kaufmann Publishers Inc.; 2000.

68. Kankainen M, Gopalacharyulu P, Holm L, Oresic M.

MPEA-metabolite pathway enrichment analysis. Bio-

informatics 2011;

ciency and additional

functions for mass spectrometry-based metabolomics

data processing. Metabolomics 2012;

improved ef

(Suppl. 1):1 e 6.

51. Baran R, Kochi H, Saito N, et al. MathDAMP: a package

for differential analysis of metabolite pro

les. BMC

:1878 e 9.

69. Boccard J, Badoud F, Grata E, et al. A steroidomic

approach for biomarkers discovery in doping control.

Forensic Sci Int 2011;

:530.

52. Bellew M, Coram M, Fitzgibbon M, et al. A suite of

algorithms for the comprehensive analysis of complex

protein mixtures using high-resolution LC-MS. Bio-

informatics 2006;

Bioinformatics 2006;

:85 e 94.

70. Hendrickx DM, Hoefsloot HCJ, Hendriks MMWB,

Canelas AB, Smilde AK. Global test for metabolic

pathway differences between conditions. Anal Chim

Acta 2012;

213

:1902 e 9.

53. Kohlbacher O, Reinert K, Gropl C, et al. TOPP d the

OpenMS proteomics pipeline. Bioinformatics 2007;

:8 e 15.

71. Broadhurst DI, Kell DB. Statistical strategies for

avoiding false discoveries in metabolomics and related

experiments. Metabolomics 2006;

719

E191 e 7.

54. Hiller K, Hangebrauk J, Jager C, Spura J, Schreiber K,

Schomburg D. MetaboliteDetector: comprehensive

analysis tool for targeted and nontargeted GC/MS

based metabolome analysis. Anal Chem 2009;

:171 e 96.

72. Holmes E, Antti H. Chemometric contributions to the

evolution of metabonomics: mathematical solutions to

characterising and interpreting complex biological

NMR spectra. Analyst 2002;

3429 e 39.

55. Sadygov RG, Maroto FM, Huhmer AFR. ChromAlign:

A two-step algorithmic procedure for time alignment

of three-dimensional LC-MS chromatographic surfaces.

Anal Chem 2006;

:1549 e 57.

73. Hotelling H. Analysis of a complex of statistical vari-

ables into principal components. J Educ Psychol 1933;

127

:8207 e 17.

56. Mitchell TM. Machine Learning . New York: McGraw

Hill; 1997.

57. Yu L, Liu H. Ef

:417 e 41.

74. Pearson K. On lines and planes of closest

t to systems

:559 e 72.

75. Comon P. Independent component analysis, A new

concept? Signal Process 1994;

of points in space. Philos Mag 1901;

cient feature selection via analysis of

relevance and redundancy. J Mach Learn Res 2004;

:287 e 314.

76. Scholz M, Gatzek S, Sterling A, Fiehn O, Selbig J.

Metabolite

1205 e 24.

58. Hall MA, Holmes G. Benchmarking attribute selection

techniques for discrete class data mining. IEEE Trans

Knowl Data Eng 2003;

fingerprinting: detecting biological features

by independent component analysis. Bioinformatics

2004;

:1437 e 47.

59. Clemmensen L, Hastie T, Witten D, Ersboll B. Sparse

discriminant analysis. Technometrics 2011;

:2447 e 54.

77. Bro R, Papalexakis EE, Acar E, Sidiropoulos ND.

Coclustering d a useful tool for chemometrics. J Che-

mometr 2012;

:406 e 13.

60. Guyon I, Elisseeff A. An introduction to variable and

feature selection. J Mach Learn Res 2003;

:256 e 63.

78. Hartigan JA, Wong MA. A K-means clustering algo-

rithm. Appl Stat 1979;

:1157 e 82.

61. Robnik-Sikonja M, Kononenko I. Theoretical and

empirical analysis of ReliefF and RReliefF. Mach Learn

2003;

:100 e 8.

79. Dunn JC. A fuzzy relative of the ISODATA process

and its use in detecting compact well-separated clus-

ters. Cybern Syst 1973;

:23 e 69.

62. Kullback S. An application of information theory to

multivariate analysis. Ann Math Stat 1952;

:32 e 57.

80. Wold S, Sjostrom M, Eriksson L. PLS-regression:

a basic tool of chemometrics. Chemom Intell Lab Syst

2001;

:88 e 102.

63. Kohavi R, John GH. Wrappers for feature subset

selection. Artif Intell 1997;

:273 e 324.

:109 e 30.

Proteomic and Metabolomic Approaches to Biomarker Discovery

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