-
1
-
-
77949507309
-
Robust biomarker identification for cancer diagnosis with ensemble feature selection methods
-
Abeel, T. et al. (2010) Robust biomarker identification for cancer diagnosis with ensemble feature selection methods. Bioinformatics, 26, 392-398.
-
(2010)
Bioinformatics
, vol.26
, pp. 392-398
-
-
Abeel, T.1
-
2
-
-
0034598746
-
Distinct types of di?use large B-cell lymphoma identi{thorn}ed by gene expression pro{thorn}ling
-
Alizadeh, A. et al. (2000) Distinct types of di?use large B-cell lymphoma identi{thorn}ed by gene expression pro{thorn}ling. Nature, 403, 503-511.
-
(2000)
Nature
, vol.403
, pp. 503-511
-
-
Alizadeh, A.1
-
3
-
-
77952814988
-
Permutation importance: a corrected feature importance measure
-
Altmann, A. et al. (2010) Permutation importance: a corrected feature importance measure. Bioinformatics, 26, 1340-1347.
-
(2010)
Bioinformatics
, vol.26
, pp. 1340-1347
-
-
Altmann, A.1
-
4
-
-
0037076322
-
Selection bias in gene extraction on the basis of microarray gene-expression data
-
Ambroise, C. and McLachlan, G.J. (2002) Selection bias in gene extraction on the basis of microarray gene-expression data. Proc. Nati. Acad. Sci., 99, 6562-6566.
-
(2002)
Proc. Nati. Acad. Sci.
, vol.99
, pp. 6562-6566
-
-
Ambroise, C.1
McLachlan, G.J.2
-
5
-
-
0001677717
-
Controlling the false discovery rate: A practical and powerful approach to multiple testing
-
Benjamini, Y. and Hochberg, Y. (1995) Controlling the false discovery rate: A practical and powerful approach to multiple testing. J. Roy. Stat. Soci., Ser. B (Methodol.), 57, 289-300.
-
(1995)
J. Roy. Stat. Soci., Ser. B (Methodol.)
, vol.57
, pp. 289-300
-
-
Benjamini, Y.1
Hochberg, Y.2
-
6
-
-
0026966646
-
Atraining algorithm for optimal margin classifiers
-
Haussler, D. (ed.), Pittsburgh, ACM Press
-
Boser, B.E. et al. (1992)Atraining algorithm for optimal margin classifiers. In Haussler, D. (ed.), Proceedings of the 5th Annual ACMWorkshop on Computational Learning Theory, Pittsburgh, ACM Press, pp. 144-152.
-
(1992)
Proceedings of the 5th Annual ACMWorkshop on Computational Learning Theory
, pp. 144-152
-
-
Boser, B.E.1
-
7
-
-
0035478854
-
Random forests
-
Breiman, L. (2001) Random forests. Mach. Learn., 45, 5-32.
-
(2001)
Mach. Learn.
, vol.45
, pp. 5-32
-
-
Breiman, L.1
-
9
-
-
0035939903
-
Delineation of prognostic biomarkers in prostate cancer
-
Dhanasekaran, S. et al. (2001) Delineation of prognostic biomarkers in prostate cancer. Nature, 412, 822-826.
-
(2001)
Nature
, vol.412
, pp. 822-826
-
-
Dhanasekaran, S.1
-
12
-
-
55349093320
-
Some step-down procedures controlling the false discovery rate under dependence
-
Ge, Y. et al. (2008) Some step-down procedures controlling the false discovery rate under dependence. Stati. Sin., 18, 881-904.
-
(2008)
Stati. Sin.
, vol.18
, pp. 881-904
-
-
Ge, Y.1
-
13
-
-
25144482428
-
Proteomic mass spectra classification using decision tree based ensemble methods
-
Geurts, P. et al. (2005) Proteomic mass spectra classification using decision tree based ensemble methods. Bioinformatics, 21, 3138-3145.
-
(2005)
Bioinformatics
, vol.21
, pp. 3138-3145
-
-
Geurts, P.1
-
14
-
-
33646430006
-
Extremely randomized trees
-
Geurts, P. et al. (2006) Extremely randomized trees. Mach. Learn., 36, 3-42.
-
(2006)
Mach. Learn.
, vol.36
, pp. 3-42
-
-
Geurts, P.1
-
15
-
-
0033569406
-
Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring
-
Golub, T.R. et al. (1999) Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring. Science, 286, 531-537.
-
(1999)
Science
, vol.286
, pp. 531-537
-
-
Golub, T.R.1
-
16
-
-
0036161259
-
Gene selection for cancer classification using support vector machines
-
Guyon, I. et al. (2002) Gene selection for cancer classification using support vector machines. Mach. Learn., 46, 389-422.
-
(2002)
Mach. Learn.
, vol.46
, pp. 389-422
-
-
Guyon, I.1
-
18
-
-
77958449045
-
Stable feature selection for biomarker discovery
-
He, Z. andYu, W. (2010) Stable feature selection for biomarker discovery. Computational Biology and Chemistry, 34, 215-225.
-
(2010)
Computational Biology and Chemistry
, vol.34
, pp. 215-225
-
-
He, Z.1
Yu, W.2
-
19
-
-
72949114072
-
Exploiting tree-based variable importances to selectively identify relevant variables
-
Huynh-Thu, V.A. et al. (2008) Exploiting tree-based variable importances to selectively identify relevant variables. JMLR:Workshop and Conference proceedings, Antwerp, 4, 60-73.
-
(2008)
JMLR:Workshop and Conference proceedings, Antwerp
, vol.4
, pp. 60-73
-
-
Huynh-Thu, V.A.1
-
21
-
-
84890447445
-
Variable selection using svm based criteria
-
Rakotomamonjy, A. (2003) Variable selection using svm based criteria. J. Mach. Learn. Res., 3, 1357-1370.
-
(2003)
J. Mach. Learn. Res.
, vol.3
, pp. 1357-1370
-
-
Rakotomamonjy, A.1
-
22
-
-
0141990695
-
Theoretical and empirical analysis of ReliefF and RReliefF
-
Robnik-Sikonja, M. and Kononenko, I. (2003) Theoretical and empirical analysis of ReliefF and RReliefF. Mach. Learn. J., 53, 23-69.
-
(2003)
Mach. Learn. J.
, vol.53
, pp. 23-69
-
-
Robnik-Sikonja, M.1
Kononenko, I.2
-
23
-
-
35748932917
-
A review of feature selection techniques in bioinformatics
-
Saeys, Y. et al. (2007) A review of feature selection techniques in bioinformatics. Bioinformatics, 23, 2507-2517.
-
(2007)
Bioinformatics
, vol.23
, pp. 2507-2517
-
-
Saeys, Y.1
-
24
-
-
18244409933
-
Diffuse large b-cell lymphoma outcome prediction by geneexpression profiling and supervised machine learning
-
Shipp, M. et al. (2002) Diffuse large b-cell lymphoma outcome prediction by geneexpression profiling and supervised machine learning. Nat. Med., 8, 68-74.
-
(2002)
Nat. Med.
, vol.8
, pp. 68-74
-
-
Shipp, M.1
-
25
-
-
19044391072
-
Gene expression correlates of clinical prostate cancer behavior
-
Singh, D. et al. (2002) Gene expression correlates of clinical prostate cancer behavior. Cancer Cell, 1, 203-209.
-
(2002)
Cancer Cell
, vol.1
, pp. 203-209
-
-
Singh, D.1
-
26
-
-
77949539217
-
Pitfalls of supervised feature selection
-
Smialowski, P. et al. (2010) Pitfalls of supervised feature selection. Bioinformatics, 26, 440-443.
-
(2010)
Bioinformatics
, vol.26
, pp. 440-443
-
-
Smialowski, P.1
-
27
-
-
2942701493
-
Ranking a random feature for variable and feature selection
-
Stoppiglia, H. et al. (2003) Ranking a random feature for variable and feature selection. J. Mach. Learn. Res., 3, 1399-1414.
-
(2003)
J. Mach. Learn. Res.
, vol.3
, pp. 1399-1414
-
-
Stoppiglia, H.1
-
28
-
-
0042424602
-
Statistical significance for genomewide studies
-
Storey, J.D. and Tibshirani, R. (2003) Statistical significance for genomewide studies. Proc. Natl. Acad. Sci. USA, 100, 9440-9445.
-
(2003)
Proc. Natl. Acad. Sci. USA
, vol.100
, pp. 9440-9445
-
-
Storey, J.D.1
Tibshirani, R.2
-
29
-
-
68949154557
-
Feature selection with ensembles, artificial variables, and redundancy elimination
-
Tuv, E. et al. (2009) Feature selection with ensembles, artificial variables, and redundancy elimination. J. Mach. Learn. Res., 10, 1341-1366.
-
(2009)
J. Mach. Learn. Res.
, vol.10
, pp. 1341-1366
-
-
Tuv, E.1
-
30
-
-
13844310310
-
Gene-expression profiles to predict distant metastasis of lymphnode-negative primary breast cancer
-
Wang, Y. et al. (2005) Gene-expression profiles to predict distant metastasis of lymphnode-negative primary breast cancer. Lancet, 365, 671-679.
-
(2005)
Lancet
, vol.365
, pp. 671-679
-
-
Wang, Y.1
-
32
-
-
33747424762
-
Significance of gene ranking for classification of microarray samples
-
Zhang, C. et al. (2006) Significance of gene ranking for classification of microarray samples. IEEE/ACM Trans. Comput. Biol. Bioinform., 3, 1-9.
-
(2006)
IEEE/ACM Trans. Comput. Biol. Bioinform.
, vol.3
, pp. 1-9
-
-
Zhang, C.1
|