-
1
-
-
35748932917
-
Larranaga. A review of feature selection techniques in bioinformatics
-
Saeys Y, Inza I, Larranaga. A review of feature selection techniques in bioinformatics. Bioinformatics. 2007;23:2507-17.
-
(2007)
Bioinformatics
, vol.23
, pp. 2507-2517
-
-
Saeys, Y.1
Inza, I.2
-
2
-
-
27144489164
-
A tutorial on support vector machines for pattern recognition
-
Burges CJC. A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery. 1998;2:121-67.
-
(1998)
Data Mining and Knowledge Discovery
, vol.2
, pp. 121-167
-
-
Burges, C.J.C.1
-
4
-
-
30644464444
-
Gene selection and classification of microarray data using random forest
-
Díaz-Uriarte R, Alvarez de Andrés S. Gene selection and classification of microarray data using random forest. BMC Bioinformatics. 2006;7:3.
-
(2006)
BMC Bioinformatics
, vol.7
, pp. 3
-
-
Díaz-Uriarte, R.1
Alvarez de Andrés, S.2
-
5
-
-
84869422419
-
A comparative study of classification methods for microarray data analysis
-
Hu H, Li J, Plank A, Wang H, Daggard G. A comparative study of classification methods for microarray data analysis. Proc Fifth Australasian Data Mining Conference. 2006;61:33-7.
-
(2006)
Proc Fifth Australasian Data Mining Conference
, vol.61
, pp. 33-37
-
-
Hu, H.1
Li, J.2
Plank, A.3
Wang, H.4
Daggard, G.5
-
6
-
-
0036161259
-
Gene selection for cancer classification using support vector machines
-
Guyon I, Weston J, Barnhill S. Gene selection for cancer classification using support vector machines. Machine Learning. 2002;46:389-422.
-
(2002)
Machine Learning
, vol.46
, pp. 389-422
-
-
Guyon, I.1
Weston, J.2
Barnhill, S.3
-
7
-
-
0141738784
-
Comparison of statistical methods for classification of ovarian cancer using mass spectrometry data
-
Wu B, Abbott T, Fishman D, McMurray W, Mor G, Stone K, et al. Comparison of statistical methods for classification of ovarian cancer using mass spectrometry data. Bioinformatics. 2003;19:1636-43.
-
(2003)
Bioinformatics
, vol.19
, pp. 1636-1643
-
-
Wu, B.1
Abbott, T.2
Fishman, D.3
McMurray, W.4
Mor, G.5
Stone, K.6
-
8
-
-
25144482428
-
Proteomic mass spectra classification using decision tree based ensemble methods
-
Geurts P, Fillet M, de Seny D, Meuwis M-A, Malaise M, et al. Proteomic mass spectra classification using decision tree based ensemble methods. Bioinformatics. 2005;21:3138-45.
-
(2005)
Bioinformatics
, vol.21
, pp. 3138-3145
-
-
Geurts, P.1
Fillet, M.2
de Seny, D.3
Meuwis, M.-A.4
Malaise, M.5
-
9
-
-
46149101211
-
Classification of premalignant pancreatic cancer massspectrometry data using decision tree ensembles
-
Ge G, Wong GW. Classification of premalignant pancreatic cancer massspectrometry data using decision tree ensembles. BMC Bioinformatics. 2008;9:275.
-
(2008)
BMC Bioinformatics
, vol.9
, pp. 275
-
-
Ge, G.1
Wong, G.W.2
-
10
-
-
67650527108
-
Comparison of feature selection and classification for MALDI-MS data
-
Liu Q, Sung AH, Qiao M, Chen Z, Yang JY, et al. Comparison of feature selection and classification for MALDI-MS data. BMC Genomics. 2009;10(Suppl 1):S3.
-
(2009)
BMC Genomics
, vol.10
, Issue.SUPPL. 1
-
-
Liu, Q.1
Sung, A.H.2
Qiao, M.3
Chen, Z.4
Yang, J.Y.5
-
11
-
-
70349731767
-
Ovarian cancer detection from metabolomic liquid chromatography/mass spectrometry data by support vector machines
-
Guan W, Zhou M, Hampton CY, Benigno BB, Walker LD, et al. Ovarian cancer detection from metabolomic liquid chromatography/mass spectrometry data by support vector machines. BMC Bioinformatics. 2009;10: 259.
-
(2009)
BMC Bioinformatics
, vol.10
, pp. 259
-
-
Guan, W.1
Zhou, M.2
Hampton, C.Y.3
Benigno, B.B.4
Walker, L.D.5
-
12
-
-
33646377650
-
Recursive SVM feature selection and sample classification for mass-spectrometry and microarray data
-
Zhang X, Lu X, Shi Q, Xu X, Leung HE, et al. Recursive SVM feature selection and sample classification for mass-spectrometry and microarray data. BMC Bioinformatics. 2002;7:197.
-
(2002)
BMC Bioinformatics
, vol.7
, pp. 197
-
-
Zhang, X.1
Lu, X.2
Shi, Q.3
Xu, X.4
Leung, H.E.5
-
13
-
-
0033569406
-
Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring
-
Golub TR, Slonin DK, Tamayo P, et al. Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring. Science. 1999;286:531-7.
-
(1999)
Science
, vol.286
, pp. 531-537
-
-
Golub, T.R.1
Slonin, D.K.2
Tamayo, P.3
-
14
-
-
0036489046
-
Comparison of discrimination methods for the classification of tumors using gene expression data
-
Dudroit S, Fridlyand J, Speed TP. Comparison of discrimination methods for the classification of tumors using gene expression data. American Statistical Association. 2002;97:77-87.
-
(2002)
American Statistical Association
, vol.97
, pp. 77-87
-
-
Dudroit, S.1
Fridlyand, J.2
Speed, T.P.3
-
15
-
-
33748684439
-
On the statistical assessment of classifiers using DNA microarray data
-
Ancona N, Magletta R, Piepoli A, D'Addabbo A, Cotungo R, et al. On the statistical assessment of classifiers using DNA microarray data. BMC Bioinformatics. 2006;7:387.
-
(2006)
BMC Bioinformatics
, vol.7
, pp. 387
-
-
Ancona, N.1
Magletta, R.2
Piepoli, A.3
D'addabbo, A.4
Cotungo, R.5
-
16
-
-
0034794796
-
Expression profiling of medulloblastoma: PDGFR and the RAW/MAPK pathway as therapeutic targets for metsastic disease
-
MacDonald TJ, Brown KM, LeFleur B, Peterson K, Lawlor C, et al. Expression profiling of medulloblastoma: PDGFR and the RAW/MAPK pathway as therapeutic targets for metsastic disease. Nature Genetics. 2001;29: 143-52.
-
(2001)
Nature Genetics
, vol.29
, pp. 143-152
-
-
Macdonald, T.J.1
Brown, K.M.2
Lefleur, B.3
Peterson, K.4
Lawlor, C.5
-
17
-
-
13444249852
-
Prediction of cancer outcome with microarrays: A multiple random validation strategy
-
Michelis S, Koscielny S, Hill C. Prediction of cancer outcome with microarrays: a multiple random validation strategy. Lancet. 2005;365:488-92.
-
(2005)
Lancet
, vol.365
, pp. 488-492
-
-
Michelis, S.1
Koscielny, S.2
Hill, C.3
-
18
-
-
69249159556
-
Development of biomarker classifiers from high-dimensional data
-
Baek S, Tsai C-A, Chen JJ. Development of biomarker classifiers from high-dimensional data. Briefings in Bioinformatics. 2009;10:537-46.
-
(2009)
Briefings In Bioinformatics
, vol.10
, pp. 537-546
-
-
Baek, S.1
Tsai, C.-A.2
Chen, J.J.3
-
19
-
-
0036735386
-
Translation of microarray data into clinically relevant cancer diagnostic tests using gene expression ratios in lung cancer and mesothelioma
-
Gordan GJ, Jensen RV, Hsiao L, Gullans SR, Blumenstock JE, et al. Translation of microarray data into clinically relevant cancer diagnostic tests using gene expression ratios in lung cancer and mesothelioma. Cancer Research. 2002;62:4963-7.
-
(2002)
Cancer Research
, vol.62
, pp. 4963-4967
-
-
Gordan, G.J.1
Jensen, R.V.2
Hsiao, L.3
Gullans, S.R.4
Blumenstock, J.E.5
-
20
-
-
0037120949
-
Serum proteomic patterns for detection of prostate cancer
-
Petricoin EF, Ornstein DK, Paweletz CP, Ardekani A, Hackett PS, et al. Serum proteomic patterns for detection of prostate cancer. Journal of the National Cancer Institute. 2002;94:1576-8.
-
(2002)
Journal of the National Cancer Institute
, vol.94
, pp. 1576-1578
-
-
Petricoin, E.F.1
Ornstein, D.K.2
Paweletz, C.P.3
Ardekani, A.4
Hackett, P.S.5
-
21
-
-
0345040873
-
Classification and Regression by random Forest
-
Liaw A, Wiener M. Classification and Regression by random Forest. R News. 2002;2:18-22.
-
(2002)
R News
, vol.2
, pp. 18-22
-
-
Liaw, A.1
Wiener, M.2
-
22
-
-
79951480123
-
-
R Development Core Team. R Foundation for Statistical Computing, Vienna, Austria; ISBN 3-900051-07-0, URL
-
R Development Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria; 2010, ISBN 3-900051-07-0, URL http://www.R-project.org.
-
(2010)
R: A language and environment for statistical computing
-
-
-
23
-
-
2942519065
-
-
2010; TU Wien. R package version 1
-
Dimitriadou E, Hornik K, Leisch F, Meyer D, Weingessel A. e1071: Misc Functions of the Department of Statistics (e1071). 2010; TU Wien. R package version 1.5-24. http://CRAN.R-project.org/package=e1071.
-
E1071: Misc Functions of the Department of Statistics (e1071)
, pp. 5-24
-
-
Dimitriadou, E.1
Hornik, K.2
Leisch, F.3
Meyer, D.4
Weingessel, A.5
-
24
-
-
84950461478
-
Estimating the error rate of a prediction rule: Improvements on cross-validation
-
Efron B. Estimating the error rate of a prediction rule: improvements on cross-validation. Journal of the American Statistical Association. 1983;78: 316-31.
-
(1983)
Journal of the American Statistical Association
, vol.78
, pp. 316-331
-
-
Efron, B.1
-
25
-
-
77954168391
-
Effect of training-sample size and classification difficulty on the accuracy of genomic predictors
-
Popovici V, Chen W, Gallas BG, Hatzis C, Shi W, et al. Effect of training-sample size and classification difficulty on the accuracy of genomic predictors. Breast Cancer Research. 2010;12:R5.
-
(2010)
Breast Cancer Research
, vol.12
-
-
Popovici, V.1
Chen, W.2
Gallas, B.G.3
Hatzis, C.4
Shi, W.5
-
26
-
-
33749030973
-
Identifying genes that contribute most to good classification in microarrays
-
Baker SG, Kramer BS. Identifying genes that contribute most to good classification in microarrays. BMC Bioinformatics. 2006;7:407.
-
(2006)
BMC Bioinformatics
, vol.7
, pp. 407
-
-
Baker, S.G.1
Kramer, B.S.2
|