Biology Reference
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81. Daszykowski M, Walczak B, Massart DL. Projection
methods in chemistry. Chemom Intell Lab Syst 2003;
97. Hop
eld JJ. Neural networks and physical systems
with emergent collective computational abilities. Proc
Natl Acad Sci USA 1982;
:
65
97 e 112.
82. Wold S, Ruhe A, Wold H, Dunn WJ. The collinearity
problem in linear-regression d the partial least-squares
(PLS) approach to generalized inverses. Siam J Sci Stat
Comput 1984;
:2554 e 8.
98. Taylor J, King RD, Altmann T, Fiehn O. Application of
metabolomics to plant genotype discrimination using
statistics and machine learning. Bioinformatics 2002;
79
:
18
:735 e 43.
S241 e 8.
99. Vapnik VN. An overview of statistical learning theory.
IEEE Trans Neural Netw 1999;
5
83.
Jonsson P, Bruce SJ, Moritz T, et al. Extraction, inter-
pretation and validation of information for comparing
samples in metabolic LC/MS data sets. Analyst 2005;
130
:988 e 99.
100. Keerthi SS, Gilbert EG. Convergence of a generalized
SMO algorithm for SVM classi
10
:701 e 7.
84. Trygg J, Wold S. Orthogonal projections to latent
structures (O-PLS). J Chemometr 2002;
er design. Mach Learn
:351 e 60.
101. Platt J. How to implement SVMs. IEEE Intell Syst 1998;
13
2002;
46
:119 e 28.
85. Trygg J. O2-PLS for qualitative and quantitative
analysis in multivariate calibration. J Chemometr 2002;
16
16
:26 e 8.
102. Boccard J, Kalousis A, Hilario M, et al. Standard
machine learning algorithms applied to UPLC-TOF/
MS metabolic
:283 e 93.
86. Major HJ, Williams R, Wilson AJ, Wilson ID. A
metabonomic analysis of plasma from Zucker rat
strains using gas chromatography/mass spectrometry
and pattern recognition. Rapid Commun Mass Spectrom
2006;
fingerprinting for the discovery of
wound biomarkers in Arabidopsis thaliana. Chemom
Intell Lab Syst 2010;
:20 e 7.
103. Mahadevan S, Shah SL, Marrie TJ, Slupsky CM.
Analysis of metabolomic data using support vector
machines. Anal Chem 2008;
104
:3295 e 302.
87. Quinlan JR. Improved use of continuous attributes in
C4.5. J Artif Intell Res 1996;
20
:7562 e 70.
104. Beckonert O, Bollard ME, Ebbels TMD, et al.
NMR-based metabonomic toxicity classi
80
:77 e 90.
88. Breiman L, Friedman JH, Olshen RA, Stone CJ. Clas-
si
4
cation: hier-
archical cluster analysis and k-nearest-neighbour
approaches. Anal Chim Acta 2003;
cation and Regression Trees . Monterey, CA: Wads-
worth & Brooks; 1984.
89. Zheng ZJ. Constructing conjunctions using systematic
search on decision trees. Knowl Base Syst 1998;
:3 e 15.
105. Correa E, Goodacre R. A genetic algorithm-
Bayesian network approach for the analysis of
metabolomics and spectroscopic data: application
to the rapid identi
490
:
10
421 e 30.
90. Gama J. Oblique linear tree. In: Liu X, Cohen P,
Berthold M, editors. Advances in Intelligent Data Anal-
ysis Reasoning about Data . Berlin/Heidelberg: Springer;
1997.
91. Breiman L. Random forests. Mach Learn 2001;
cation of Bacillus spores and
classi
cation of Bacillus species. BMC Bioinformatics
:33.
106. Gavai AK, Tikunov Y, Ursem R, et al. Constraint-
based probabilistic learning of metabolic pathways
from tomato volatiles. Metabolomics 2009;
2011;
12
:
45
5 e 32.
92. Shawe-Taylor J, Cristianini N. Kernel Methods for
Pattern Analysis . New York: Cambridge University
Press; 2004.
93. Schölkopf B, Smola A, Müller KR. Nonlinear compo-
nent analysis as a kernel eigenvalue problem. Neural
Comput 1998;
:419 e 28.
107. Yetukuri L, Tikka J, Hollmen J, Oresic M. Functional
prediction of unidenti
5
ed lipids using supervised
:18 e 26.
108. Wiener MC, Sachs JR, Deyanova EG, Yates NA.
Differential mass spectrometry: a label-free LC-MS
method for
classi
ers. Metabolomics 2010;
6
:1299 e 319.
94. Lindgren F, Geladi P, Wold S. The kernel algorithm
for PLS. J Chemometr 1993;
cant differences in complex
peptide and protein mixtures. Anal Chem 2004;
finding signi
10
:
76
:45 e 59.
95. Rännar S, Lindgren F, Geladi P, Wold S. A PLS kernel
algorithm for data sets with many variables and fewer
objects. 1. Theory and algorithm. J Chemometr 1994;
7
6085 e 96.
109. Shaffer JP. Multiple hypothesis-testing. Annu Rev
Psychol 1995;
:561 e 84.
110. Benjamini Y, Hochberg Y. Controlling the false
discovery rate d a practical and powerful approach to
multiple testing. J R Stat Soc Series B Stat Methodol
1995;
46
8
:
111 e 25.
96. Rantalainen M, Bylesjo M, Cloarec O, Nicholson JK,
Holmes E, Trygg J. Kernel-based orthogonal projec-
tions to latent structures (K-OPLS). J Chemometr 2007;
21
:289 e 300.
111. Fawcett T. An introduction to ROC analysis. Pattern
Recognit Lett 2006;
57
:376 e 85.
:861 e 74.
27
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