Biology Reference
In-Depth Information
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
Search WWH ::
Custom Search