-
2
-
-
41649098547
-
Classification of breast cancer versus normal samples from mass spectrometry profiles using linear discriminant analysis of important features selected by random forest
-
Datta S. Classification of breast cancer versus normal samples from mass spectrometry profiles using linear discriminant analysis of important features selected by random forest. Stat Appl Genet Mol Biol. 2008;7.
-
(2008)
Stat Appl Genet Mol Biol
, pp. 7
-
-
Datta, S.1
-
3
-
-
77954083699
-
Gene expression profiling of peripheral blood cells for early detection of breast cancer
-
Aaroe J, Lindahl T, Dumeaux V, Sabo S, Tobin D, Hagen N, et al. Gene expression profiling of peripheral blood cells for early detection of breast cancer. Breast Cancer Res. 2010;12:R7.
-
(2010)
Breast Cancer Res
, vol.12
-
-
Aaroe, J.1
Lindahl, T.2
Dumeaux, V.3
Sabo, S.4
Tobin, D.5
Hagen, N.6
-
4
-
-
80052567230
-
Developing risk prediction models for type 2 diabetes: a systematic review of methodology and reporting
-
Collins G, Mallett S, Omar O, Yu LM. Developing risk prediction models for type 2 diabetes: a systematic review of methodology and reporting. BMC Med. 2011; 9:103.
-
(2011)
BMC Med
, vol.9
, pp. 103
-
-
Collins, G.1
Mallett, S.2
Omar, O.3
Yu, L.M.4
-
5
-
-
0347181849
-
A data review and re-assessment of ovarian cancer serum proteomic profiling
-
Sorace JM, Zhan M. A data review and re-assessment of ovarian cancer serum proteomic profiling. BMC Bioinforma. 2003; 4:24.
-
(2003)
BMC Bioinforma
, vol.4
, pp. 24
-
-
Sorace, J.M.1
Zhan, M.2
-
6
-
-
85168638570
-
Advances in mass spectrometry-based technologies to direct personalized medicine in ovarian cancer
-
Leung F, Musrap N, Diamandis EP, Kulasingam V. Advances in mass spectrometry-based technologies to direct personalized medicine in ovarian cancer. Adv Integr Med. 2013; 1:74-86.
-
(2013)
Adv Integr Med
, vol.1
, pp. 74-86
-
-
Leung, F.1
Musrap, N.2
Diamandis, E.P.3
Kulasingam, V.4
-
7
-
-
79952415527
-
Taming the dragon: genomic biomarkers to individualize the treatment of cancer
-
Majewski IJ, Bernards R. Taming the dragon: genomic biomarkers to individualize the treatment of cancer. Nat Med. 2011;304-12.
-
(2011)
Nat Med
, pp. 304-312
-
-
Majewski, I.J.1
Bernards, R.2
-
8
-
-
84877278637
-
Implementing personalized cancer genomics in clinical trials
-
Simon R, Roychowdhury S. Implementing personalized cancer genomics in clinical trials. Nat Rev Drug Discov. 2013; 12(5):358-69.
-
(2013)
Nat Rev Drug Discov
, vol.12
, Issue.5
, pp. 358-369
-
-
Simon, R.1
Roychowdhury, S.2
-
10
-
-
18244409933
-
Diffuse large B-cell lymphoma outcome prediction by gene-expression profiling and supervised machine learning
-
Shipp MA, Ross KN, Tamayo P, Weng AP, Kutok JL, Aguiar RC, et al. Diffuse large B-cell lymphoma outcome prediction by gene-expression profiling and supervised machine learning. Nat Med. 2002; 8:68.
-
(2002)
Nat Med
, vol.8
, pp. 68
-
-
Shipp, M.A.1
Ross, K.N.2
Tamayo, P.3
Weng, A.P.4
Kutok, J.L.5
Aguiar, R.C.6
-
11
-
-
0037443891
-
Oligonucleotide microarray for prediction of early intrahepatic recurrence of hepatocellular carcinoma after curative resection
-
Iizuka N, Oka M, Yamada-Okabe H, Nishida M, Maeda Y, Mori N, et al. Oligonucleotide microarray for prediction of early intrahepatic recurrence of hepatocellular carcinoma after curative resection. The Lancet. 2003; 361(9361):923-9.
-
(2003)
The Lancet
, vol.361
, Issue.9361
, pp. 923-929
-
-
Iizuka, N.1
Oka, M.2
Yamada-Okabe, H.3
Nishida, M.4
Maeda, Y.5
Mori, N.6
-
12
-
-
84880059657
-
The electronic medical records and genomics (eMERGE) network: past, present, and future
-
Gottesman O, Kuivaniemi H, Tromp G, Faucett WA, Li R, Manolio TA, et al. The electronic medical records and genomics (eMERGE) network: past, present, and future. Genet Med. 2013; 15(10):761-71.
-
(2013)
Genet Med
, vol.15
, Issue.10
, pp. 761-771
-
-
Gottesman, O.1
Kuivaniemi, H.2
Tromp, G.3
Faucett, W.A.4
Li, R.5
Manolio, T.A.6
-
13
-
-
84893783009
-
Predicting stroke through genetic risk functions The CHARGE risk score project
-
Ibrahim-Verbaas CA, Fornage M, Bis JC, Choi SH, Psaty BM, Meigs JB, et al. Predicting stroke through genetic risk functions The CHARGE risk score project. Stroke. 2014; 45(2):403-12.
-
(2014)
Stroke
, vol.45
, Issue.2
, pp. 403-412
-
-
Ibrahim-Verbaas, C.A.1
Fornage, M.2
Bis, J.C.3
Choi, S.H.4
Psaty, B.M.5
Meigs, J.B.6
-
14
-
-
58149330446
-
Breast cancer diagnosis from proteomic mass spectrometry data: a comparative evaluation
-
J HD. Breast cancer diagnosis from proteomic mass spectrometry data: a comparative evaluation. Stat Appl Genet Mol Biol. 2008; 7(2):1-23.
-
(2008)
Stat Appl Genet Mol Biol
, vol.7
, Issue.2
, pp. 1-23
-
-
J, H.D.1
-
15
-
-
84906222536
-
Genetic-based prediction of disease traits: prediction is very difficult, especially about the future
-
Schrodi SJ, Mukherjee S, Shan Y, Tromp G, Sninsky JJ, Callear AP, et al. Genetic-based prediction of disease traits: prediction is very difficult, especially about the future. Front Genet.2014;5.
-
(2014)
Front Genet
, pp. 5
-
-
Schrodi, S.J.1
Mukherjee, S.2
Shan, Y.3
Tromp, G.4
Sninsky, J.J.5
Callear, A.P.6
-
16
-
-
77955622534
-
An adaptive optimal ensemble classifier via bagging and rank aggregation with applications to high dimensional data
-
Datta S, Pihur V, Datta S. An adaptive optimal ensemble classifier via bagging and rank aggregation with applications to high dimensional data. BMC Bioinforma. 2010; 11:427.
-
(2010)
BMC Bioinforma
, vol.11
, pp. 427
-
-
Datta, S.1
Pihur, V.2
Datta, S.3
-
17
-
-
84923206447
-
Evaluation of an ensemble of genetic models for prediction of a quantitative trait
-
Milton JN, Steinberg MH, Sebastiani P. Evaluation of an ensemble of genetic models for prediction of a quantitative trait. Front Genet. 2014;5.
-
(2014)
Front Genet
, pp. 5
-
-
Milton, J.N.1
Steinberg, M.H.2
Sebastiani, P.3
-
18
-
-
0030211964
-
Bagging predictors
-
Breiman L. Bagging predictors. Mach Learn. 1996; 24:123-40.
-
(1996)
Mach Learn
, vol.24
, pp. 123-140
-
-
Breiman, L.1
-
21
-
-
84862515469
-
A review on ensembles for the class imbalance problem: bagging-, boosting-, and hybrid-based approaches
-
Galar M, Fernandez A, Barrenechea E, Bustince H, Herrera F. A review on ensembles for the class imbalance problem: bagging-, boosting-, and hybrid-based approaches. Systems, Man, and Cybernetics, Part C Appl Rev IEEE Trans. 2012; 42(4):463-84.
-
(2012)
Systems, Man, and Cybernetics, Part C Appl Rev IEEE Trans
, vol.42
, Issue.4
, pp. 463-484
-
-
Galar, M.1
Fernandez, A.2
Barrenechea, E.3
Bustince, H.4
Herrera, F.5
-
22
-
-
0033692876
-
Tissue classification with gene expression profiles
-
Ben-Dor A, Bruhn L, Laboratories A, Friedman N, Schummer M, Nachman I, et al. Tissue classification with gene expression profiles. J Comput Biol. 2000; 7:559-84.
-
(2000)
J Comput Biol
, vol.7
, pp. 559-584
-
-
Ben-Dor, A.1
Bruhn, L.2
Laboratories, A.3
Friedman, N.4
Schummer, M.5
Nachman, I.6
-
23
-
-
0036489046
-
Comparison of discrimination methods for the classification of tumors using gene expression data
-
Dudoit S, Fridlyand J, Speed TP. Comparison of discrimination methods for the classification of tumors using gene expression data. J Am Stat Assoc. 2002; 97(457):77 - 87.
-
(2002)
J Am Stat Assoc
, vol.97
, Issue.457
, pp. 77-87
-
-
Dudoit, S.1
Fridlyand, J.2
Speed, T.P.3
-
25
-
-
77952337302
-
An experimental evaluation of boosting methods for classification
-
Stollhoff R, Sauerbrei W, Schumacher M. An experimental evaluation of boosting methods for classification. Methods Inform Med. 2010; 49(3):219-29.
-
(2010)
Methods Inform Med
, vol.49
, Issue.3
, pp. 219-229
-
-
Stollhoff, R.1
Sauerbrei, W.2
Schumacher, M.3
-
26
-
-
84874545393
-
DNdisorder: predicting protein disorder using boosting and deep networks
-
Eickholt J, Cheng J. DNdisorder: predicting protein disorder using boosting and deep networks. BMC Bioinforma. 2013; 14:88.
-
(2013)
BMC Bioinforma
, vol.14
, pp. 88
-
-
Eickholt, J.1
Cheng, J.2
-
27
-
-
84886737285
-
A novel strategy for classifying the output from an in silico vaccine discovery pipeline for eukaryotic pathogens using machine learning algorithms
-
Goodswen S, Kennedy P, Ellis J. A novel strategy for classifying the output from an in silico vaccine discovery pipeline for eukaryotic pathogens using machine learning algorithms. BMC Bioinforma. 2013; 14:315.
-
(2013)
BMC Bioinforma
, vol.14
, pp. 315
-
-
Goodswen, S.1
Kennedy, P.2
Ellis, J.3
-
28
-
-
84879435368
-
MeSH indexing based on automatically generated summaries
-
Jimeno-Yepes A, Plaza L, Mork J, Aronson A, Diaz A. MeSH indexing based on automatically generated summaries. BMC Bioinforma. 2013; 14:208.
-
(2013)
BMC Bioinforma
, vol.14
, pp. 208
-
-
Jimeno-Yepes, A.1
Plaza, L.2
Mork, J.3
Aronson, A.4
Diaz, A.5
-
29
-
-
0038391397
-
Boosting for tumor classification with gene expression data
-
Dettling M, Buhlmann P. Boosting for tumor classification with gene expression data. Bioinformatics. 2003; 19(9):1061-9.
-
(2003)
Bioinformatics
, vol.19
, Issue.9
, pp. 1061-1069
-
-
Dettling, M.1
Buhlmann, P.2
-
30
-
-
0034164230
-
Additive logistic regression: a statistical view of boosting
-
Friedman J, Hastie T, Tibshirani R. Additive logistic regression: a statistical view of boosting. Ann Stat. 2000;38(2):337-407.
-
(2000)
Ann Stat
, vol.38
, Issue.2
, pp. 337-407
-
-
Friedman, J.1
Hastie, T.2
Tibshirani, R.3
-
32
-
-
0035470889
-
Greedy function approximation: a gradient boosting machine
-
Friedman JH. Greedy function approximation: a gradient boosting machine. Ann Stat. 2000; 29:1189-232.
-
(2000)
Ann Stat
, vol.29
, pp. 1189-1232
-
-
Friedman, J.H.1
-
33
-
-
0037186544
-
Stochastic gradient boosting
-
Friedman JH. Stochastic gradient boosting. Comput Stat Data Anal. 1999; 38:367-78.
-
(1999)
Comput Stat Data Anal
, vol.38
, pp. 367-378
-
-
Friedman, J.H.1
-
34
-
-
34547142781
-
Boosting ridge regression
-
Tutz G, Binder H. Boosting ridge regression. Comput Stat Data Anal. 2007; 51(12):6044-59.
-
(2007)
Comput Stat Data Anal
, vol.51
, Issue.12
, pp. 6044-6059
-
-
Tutz, G.1
Binder, H.2
-
35
-
-
84859815011
-
Generalized additive models for location, scale and shape for high dimensional data-a flexible approach based on boosting
-
Mayr A, Fenske N, Hofner B, Kneib T, Schmid M. Generalized additive models for location, scale and shape for high dimensional data-a flexible approach based on boosting. J R Stat Soc Series C (Appl Stat). 2012; 61(3):403-27.
-
(2012)
J R Stat Soc Series C (Appl Stat)
, vol.61
, Issue.3
, pp. 403-427
-
-
Mayr, A.1
Fenske, N.2
Hofner, B.3
Kneib, T.4
Schmid, M.5
-
36
-
-
33845413755
-
Regularized linear discriminant analysis and its application in microarrays
-
Guo Y, Hastie T, Tibshirani R. Regularized linear discriminant analysis and its application in microarrays. Biostatistics. 2007; 8:86-100.
-
(2007)
Biostatistics
, vol.8
, pp. 86-100
-
-
Guo, Y.1
Hastie, T.2
Tibshirani, R.3
-
37
-
-
70450228445
-
Shrinkage-based diagonal discriminant analysis and its applications in high-dimensional data
-
Pang H, Tong T, Zhao H. Shrinkage-based diagonal discriminant analysis and its applications in high-dimensional data. Biometrics. 2009; 65(4):1021-9.
-
(2009)
Biometrics
, vol.65
, Issue.4
, pp. 1021-1029
-
-
Pang, H.1
Tong, T.2
Zhao, H.3
-
38
-
-
0042838307
-
Breast cancer classification and prognosis based on gene expression profiles from a population-based study
-
Sotiriou C, Neo SY, McShane LM, Korn EL, Long PM, Jazaeri A, et al. Breast cancer classification and prognosis based on gene expression profiles from a population-based study. Proc Nat Acad Sci USA. 2003; 100(18):10393-8.
-
(2003)
Proc Nat Acad Sci USA
, vol.100
, Issue.18
, pp. 10393-10398
-
-
Sotiriou, C.1
Neo, S.Y.2
McShane, L.M.3
Korn, E.L.4
Long, P.M.5
Jazaeri, A.6
-
39
-
-
13844310310
-
Gene-expression profiles to predict distant metastasis of lymph-node-negative primary breast cancer
-
Wang Y, Klijn JGM, Zhang Y, Sieuwerts AM, Look MP, Yang F, et al. Gene-expression profiles to predict distant metastasis of lymph-node-negative primary breast cancer. The Lancet. 2005; 365(9460):671-9.
-
(2005)
The Lancet
, vol.365
, Issue.9460
, pp. 671-679
-
-
Wang, Y.1
Klijn, J.G.M.2
Zhang, Y.3
Sieuwerts, A.M.4
Look, M.P.5
Yang, F.6
-
40
-
-
33751261643
-
Genetic reclassification of histologic grade delineates new clinical subtypes of breast cancer
-
Ivshina AV, George J, Senko O, Mow B, Putti TC, Smeds J, et al. Genetic reclassification of histologic grade delineates new clinical subtypes of breast cancer. Cancer Res. 2006; 66(21):10292-301.
-
(2006)
Cancer Res
, vol.66
, Issue.21
, pp. 10292-10301
-
-
Ivshina, A.V.1
George, J.2
Senko, O.3
Mow, B.4
Putti, T.C.5
Smeds, J.6
-
41
-
-
68549133155
-
Learning from imbalanced data
-
He H, Garcia EA. Learning from imbalanced data. IEEE Trans Knowl Data Eng. 2009; 21(9):1263-84.
-
(2009)
IEEE Trans Knowl Data Eng
, vol.21
, Issue.9
, pp. 1263-1284
-
-
He, H.1
Garcia, E.A.2
-
42
-
-
77957988489
-
Class prediction for high-dimensional class-imbalanced data
-
Blagus R, Lusa L. Class prediction for high-dimensional class-imbalanced data. BMC Bioinforma. 2010; 11:523.
-
(2010)
BMC Bioinforma
, vol.11
, pp. 523
-
-
Blagus, R.1
Lusa, L.2
-
43
-
-
33646023117
-
An introduction to ROC analysis
-
Fawcett T. An introduction to ROC analysis. Pattern Recognit Lett. 2006; 27(8):861-74.
-
(2006)
Pattern Recognit Lett
, vol.27
, Issue.8
, pp. 861-874
-
-
Fawcett, T.1
-
44
-
-
84977474146
-
A simple sequentially rejective multiple test procedure
-
Holm S. A simple sequentially rejective multiple test procedure. Scand J Stat. 1979; 6:65-70.
-
(1979)
Scand J Stat
, vol.6
, pp. 65-70
-
-
Holm, S.1
-
45
-
-
5344244656
-
-
Vienna, Austria: R Foundation for Statistical Computing
-
R Development Core Team. R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing; 2008. [ http://www.R-project.org ]. [ISBN 3-900051-07-0].
-
(2008)
R: A language and environment for statistical computing
-
-
-
46
-
-
14644395930
-
Population theory for boosting ensembles
-
Breiman L. Population theory for boosting ensembles. Ann Stat. 2004; 32:1-11.
-
(2004)
Ann Stat
, vol.32
, pp. 1-11
-
-
Breiman, L.1
-
47
-
-
33947284406
-
Boosted classification trees and class probability/quantile estimation
-
Mease D, Wyner AJ, Buja A. Boosted classification trees and class probability/quantile estimation. J Mach Learn Res. 2007; 8:409-39.
-
(2007)
J Mach Learn Res
, vol.8
, pp. 409-439
-
-
Mease, D.1
Wyner, A.J.2
Buja, A.3
-
49
-
-
84858743801
-
The importance of knowing when to stop. A sequential stopping rule for component-wise gradient boosting
-
Mayr A, Hofner B, Schmid M. The importance of knowing when to stop. A sequential stopping rule for component-wise gradient boosting. Methods Inform Med. 2012; 51(2):178-86.
-
(2012)
Methods Inform Med
, vol.51
, Issue.2
, pp. 178-186
-
-
Mayr, A.1
Hofner, B.2
Schmid, M.3
-
50
-
-
49749093449
-
Comment: Boosting algorithms Regularization, prediction and model fitting
-
Buja A, Mease D, Wyner AJ. Comment: Boosting algorithms Regularization, prediction and model fitting. Statist Sci. 2007; 22(4):506-12.
-
(2007)
Statist Sci
, vol.22
, Issue.4
, pp. 506-512
-
-
Buja, A.1
Mease, D.2
Wyner, A.J.3
-
51
-
-
41549131613
-
Evidence contrary to the statistical view of boosting
-
Mease D, Wyner A. Evidence contrary to the statistical view of boosting. J Mach Learn Res. 2008; 9:131-56.
-
(2008)
J Mach Learn Res
, vol.9
, pp. 131-156
-
-
Mease, D.1
Wyner, A.2
|