-
1
-
-
84942484786
-
Ridge regression: biased estimation for nonorthogonal problems
-
Hoerl A, Kennard R. Ridge regression: biased estimation for nonorthogonal problems. Technometrics. 1970;12:55–67. doi: 10.1080/00401706.1970.10488634
-
(1970)
Technometrics
, vol.12
, pp. 55-67
-
-
Hoerl, A.1
Kennard, R.2
-
2
-
-
85194972808
-
Regression shrinkage and selection via the lasso
-
Tibshirani R. Regression shrinkage and selection via the lasso. J R Stat Soc Ser B Methodol. 1996;58:267–288.
-
(1996)
J R Stat Soc Ser B Methodol
, vol.58
, pp. 267-288
-
-
Tibshirani, R.1
-
3
-
-
39449093646
-
Allowing for mandatory covariates in boosting estimation of sparse high-dimensional survival models
-
Binder H, Schumacher M. Allowing for mandatory covariates in boosting estimation of sparse high-dimensional survival models. BMC Bioinformatics. 2008;9(1):14. doi: 10.1186/1471-2105-9-14
-
(2008)
BMC Bioinformatics
, vol.9
, Issue.1
, pp. 14
-
-
Binder, H.1
Schumacher, M.2
-
4
-
-
34548565944
-
Predicting survival from microarray data: a comparative study
-
Bøvelstad HM, Nyågard S, Størvold HL, Aldrin M, Borgan Ø, Frigessi A, Lingjaerde OC. Predicting survival from microarray data: a comparative study. Bioinformatics. 2007;23:2080–2087. doi: 10.1093/bioinformatics/btm305
-
(2007)
Bioinformatics
, vol.23
, pp. 2080-2087
-
-
Bøvelstad, H.M.1
Nyågard, S.2
Størvold, H.L.3
Aldrin, M.4
Borgan, Ø.5
Frigessi, A.6
Lingjaerde, O.C.7
-
5
-
-
83455203586
-
Survival models with preclustered gene groups as covariates
-
Kammers K, Lang M, Hengstler JG, Schmidt M, Rahnenführer J. Survival models with preclustered gene groups as covariates. BMC Bioinformatics. 2011;12(1):478. doi: 10.1186/1471-2105-12-478
-
(2011)
BMC Bioinformatics
, vol.12
, Issue.1
, pp. 478
-
-
Kammers, K.1
Lang, M.2
Hengstler, J.G.3
Schmidt, M.4
Rahnenführer, J.5
-
6
-
-
80051855151
-
The irace package, iterated race for automatic algorithm configuration, TR/IRIDIA/
-
López-Ibáñez M, Dubois-Lacoste J, Stützle T, Birattari M. The irace package, iterated race for automatic algorithm configuration, TR/IRIDIA/y2011-004, IRIDIA, Université Libre de Bruxelles, Belgium; 2011.
-
-
-
López-Ibáñez, M.1
Dubois-Lacoste, J.2
Stützle, T.3
Birattari, M.4
-
7
-
-
84868554032
-
Sequential model-based optimization for general algorithm configuration
-
Coello-Coello CA, (ed), Berlin Heidelberg: Springer
-
Hutter F, Hoos HH, Leyton-Brown K. Sequential model-based optimization for general algorithm configuration. In: Coello-Coello CA, editor, Learning and intelligent optimization. Berlin Heidelberg: Springer; 2011, p. 507–523.
-
(2011)
Learning and intelligent optimization
, pp. 507-523
-
-
Hutter, F.1
Hoos, H.H.2
Leyton-Brown, K.3
-
8
-
-
85018371540
-
Auto-WEKA: combined selection and hyperparameter optimization of classification algorithms
-
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
-
Thornton C, Hutter F, Hoos HH, Leyton-Brown K. Auto-WEKA: combined selection and hyperparameter optimization of classification algorithms. In: Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining; 2013. p. 847–855.
-
(2013)
, pp. 847-855
-
-
Thornton, C.1
Hutter, F.2
Hoos, H.H.3
Leyton-Brown, K.4
-
9
-
-
84865609675
-
Tuning and evolution of support vector kernels
-
Koch P, Bischl B, Flasch O, Bartz-Beielstein T, Weihs C, Konen W. Tuning and evolution of support vector kernels. Evol Intell. 2012;5:153–170. doi: 10.1007/s12065-012-0073-8
-
(2012)
Evol Intell
, vol.5
, pp. 153-170
-
-
Koch, P.1
Bischl, B.2
Flasch, O.3
Bartz-Beielstein, T.4
Weihs, C.5
Konen, W.6
-
10
-
-
15044357936
-
Survival model predictive accuracy and ROC curves
-
Heagerty PJ, Zheng Y. Survival model predictive accuracy and ROC curves. Biometrics. 2005;61:92–105. doi: 10.1111/j.0006-341X.2005.030814.x
-
(2005)
Biometrics
, vol.61
, pp. 92-105
-
-
Heagerty, P.J.1
Zheng, Y.2
-
11
-
-
0000336139
-
Regression models and life-tables
-
Cox D. Regression models and life-tables. J R Stat Soc Ser B Methodol. 1972;34:187–220.
-
(1972)
J R Stat Soc Ser B Methodol
, vol.34
, pp. 187-220
-
-
Cox, D.1
-
12
-
-
16244401458
-
Regularization and variable selection via the elastic net
-
Zou H, Hastie T. Regularization and variable selection via the elastic net. J R Stat Soc Ser B Statist Methodol. 2005;67:301–320. doi: 10.1111/j.1467-9868.2005.00503.x
-
(2005)
J R Stat Soc Ser B Statist Methodol
, vol.67
, pp. 301-320
-
-
Zou, H.1
Hastie, T.2
-
13
-
-
0034164230
-
Additive logistic regression: a statistical view of boosting
-
Friedman J, Hastie T, Tibshirani R. Additive logistic regression: a statistical view of boosting. Ann Statist. 2000;28:337–407. doi: 10.1214/aos/1016218223
-
(2000)
Ann Statist
, vol.28
, pp. 337-407
-
-
Friedman, J.1
Hastie, T.2
Tibshirani, R.3
-
14
-
-
41549141939
-
Boosting algorithms: regularization, prediction and model fitting
-
Bühlmann P, Hothorn T. Boosting algorithms: regularization, prediction and model fitting. Statist Sci. 2007;22: 477–505. doi: 10.1214/07-STS242
-
(2007)
Statist Sci
, vol.22
, pp. 477-505
-
-
Bühlmann, P.1
Hothorn, T.2
-
15
-
-
84901420181
-
Chapter title. mboost: model-based boosting
-
Hothorn T, Buehlmann P, Kneib T, Schmid M, Hofner B. Chapter title. mboost: model-based boosting. R package version 2.2-3; 2013
-
(2013)
R package version
, vol.2
, pp. 2-3
-
-
Hothorn, T.1
Buehlmann, P.2
Kneib, T.3
Schmid, M.4
Hofner, B.5
-
16
-
-
63549089131
-
Boosting for high-dimensional time-to-event data with competing risks
-
Binder H, Allignol A, Schumacher M, Beyersmann J. Boosting for high-dimensional time-to-event data with competing risks. Bioinformatics. 2009;25:890–896. doi: 10.1093/bioinformatics/btp088
-
(2009)
Bioinformatics
, vol.25
, pp. 890-896
-
-
Binder, H.1
Allignol, A.2
Schumacher, M.3
Beyersmann, J.4
-
17
-
-
85030101531
-
CoxBoost: Cox models by likelihood based boosting for a single survival endpoint or competing risks
-
Binder H. CoxBoost: Cox models by likelihood based boosting for a single survival endpoint or competing risks. R package version 1.4; 2013
-
(2013)
R package version
, vol.1
, pp. 4
-
-
Binder, H.1
-
18
-
-
9844227496
-
Chi-square tests with one degree of freedom; extensions of the mantel-haenszel procedure
-
Mantel N. Chi-square tests with one degree of freedom; extensions of the mantel-haenszel procedure. J Amer Statist Assoc. 1963;58:690–700.
-
(1963)
J Amer Statist Assoc
, vol.58
, pp. 690-700
-
-
Mantel, N.1
-
19
-
-
41949115461
-
Random survival forests for R
-
Ishwaran H, Kogalur U. Random survival forests for R. R News. 2007;7:25–31.
-
(2007)
R News
, vol.7
, pp. 25-31
-
-
Ishwaran, H.1
Kogalur, U.2
-
21
-
-
35748932917
-
A review of feature selection techniques in bioinformatics
-
Saeys Y, Inza IN, Larrañaga P. A review of feature selection techniques in bioinformatics. Bioinformatics. 2007;23:2507–2517. doi: 10.1093/bioinformatics/btm344
-
(2007)
Bioinformatics
, vol.23
, pp. 2507-2517
-
-
Saeys, Y.1
Inza, I.N.2
Larrañaga, P.3
-
22
-
-
84861985508
-
A practical molecular assay to predict survival in resected non-squamous, non-small-cell lung cancer: development and international validation studies
-
Kratz JR, He J, Van Den Eeden SK, Zhu ZH, Gao W, Pham PT, Mulvihill MS, Ziaei F, Zhang H, Su B, Zhi X, Quesenberry CP, Habel LA, Deng Q, Wang Z, Zhou J, Li H, Huang MC, Yeh CC, Segal MR, Ray MR, Jones KD, Raz DJ, Xu Z, Jahan TM, Berryman D, He B, Mann MJ, Jablons DM. A practical molecular assay to predict survival in resected non-squamous, non-small-cell lung cancer: development and international validation studies. Lancet. 2012;379:823–832. doi: 10.1016/S0140-6736(11)61941-7
-
(2012)
Lancet
, vol.379
, pp. 823-832
-
-
Kratz, J.R.1
He, J.2
Van Den Eeden, S.K.3
Zhu, Z.H.4
Gao, W.5
Pham, P.T.6
Mulvihill, M.S.7
Ziaei, F.8
Zhang, H.9
Su, B.10
Zhi, X.11
Quesenberry, C.P.12
Habel, L.A.13
Deng, Q.14
Wang, Z.15
Zhou, J.16
Li, H.17
Huang, M.C.18
Yeh, C.C.19
Segal, M.R.20
Ray, M.R.21
Jones, K.D.22
Raz, D.J.23
Xu, Z.24
Jahan, T.M.25
Berryman, D.26
He, B.27
Mann, M.J.28
Jablons, D.M.29
more..
-
23
-
-
24344458137
-
Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy
-
Peng H, Long F, Ding C. Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans Pattern Anal Mach Intell. 2005;27:1226–1238. doi: 10.1109/TPAMI.2005.159
-
(2005)
IEEE Trans Pattern Anal Mach Intell
, vol.27
, pp. 1226-1238
-
-
Peng, H.1
Long, F.2
Ding, C.3
-
24
-
-
0037076272
-
Diagnosis of multiple cancer types by shrunken centroids of gene expression
-
Tibshirani R, Hastie T, Narasimhan B, Chu G. Diagnosis of multiple cancer types by shrunken centroids of gene expression. Proc Natl Acad Sci USA. 2002;99:6567–6572. doi: 10.1073/pnas.082099299
-
(2002)
Proc Natl Acad Sci USA
, vol.99
, pp. 6567-6572
-
-
Tibshirani, R.1
Hastie, T.2
Narasimhan, B.3
Chu, G.4
-
26
-
-
14644421108
-
A racing algorithm for configuring metaheuristics
-
Proceedings of the genetic and evolutionary computation conference, GECCO ’02, San Francisco, CA: Morgan Kaufmann Publishers Inc
-
Birattari M, Stützle T, Paquete L, Varrentrapp K. A racing algorithm for configuring metaheuristics. In: Proceedings of the genetic and evolutionary computation conference, GECCO ’02; San Francisco, CA: Morgan Kaufmann Publishers Inc.; 2002. p. 11–18.
-
(2002)
, pp. 11-18
-
-
Birattari, M.1
Stützle, T.2
Paquete, L.3
Varrentrapp, K.4
-
28
-
-
0036081355
-
Gene expression omnibus: NCBI gene expression and hybridization array data repository
-
Edgar R, Domrachev M, Lash AE. Gene expression omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res. 2002;30:207–210. doi: 10.1093/nar/30.1.207
-
(2002)
Nucleic Acids Res
, vol.30
, pp. 207-210
-
-
Edgar, R.1
Domrachev, M.2
Lash, A.E.3
-
29
-
-
49149129916
-
Gene expression-based survival prediction in lung adenocarcinoma: a multi-site, blinded validation study
-
Shedden K, Taylor JM, Enkemann SA, Tsao MS, Yeatman TJ, Gerald WL, Eschrich S, Jurisica I, Giordano TJ, Misek DE, Chang AC, Zhu CQ, Strumpf D, Hanash S, Shepherd FA, Ding K, Seymour L, Naoki K, Pennell N, Weir B, Verhaak R, Ladd-Acosta C, Golub T, Gruidl M, Sharma A, Szoke J, Zakowski M, Rusch V, Kris M, Viale A, Motoi N, Travis W, Conley B, Seshan VE, Meyerson M, Kuick R, Dobbin KK, Lively T, Jacobson JW, Beer DG. Gene expression-based survival prediction in lung adenocarcinoma: a multi-site, blinded validation study. Nat Med. 2008;14:822–827. doi: 10.1038/nm.1790
-
(2008)
Nat Med
, vol.14
, pp. 822-827
-
-
Shedden, K.1
Taylor, J.M.2
Enkemann, S.A.3
Tsao, M.S.4
Yeatman, T.J.5
Gerald, W.L.6
Eschrich, S.7
Jurisica, I.8
Giordano, T.J.9
Misek, D.E.10
Chang, A.C.11
Zhu, C.Q.12
Strumpf, D.13
Hanash, S.14
Shepherd, F.A.15
Ding, K.16
Seymour, L.17
Naoki, K.18
Pennell, N.19
Weir, B.20
Verhaak, R.21
Ladd-Acosta, C.22
Golub, T.23
Gruidl, M.24
Sharma, A.25
Szoke, J.26
Zakowski, M.27
Rusch, V.28
Kris, M.29
Viale, A.30
Motoi, N.31
Travis, W.32
Conley, B.33
Seshan, V.E.34
Meyerson, M.35
Kuick, R.36
Dobbin, K.K.37
Lively, T.38
Jacobson, J.W.39
Beer, D.G.40
more..
-
30
-
-
0142121516
-
Exploration, normalization, and summaries of high density oligonucleotide array probe level data
-
Irizarry RA, Hobbs B, Collin F, Beazer-Barclay YD, Antonellis KJ, Scherf U, Speed TP. Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics. 2003;4:249–264. doi: 10.1093/biostatistics/4.2.249
-
(2003)
Biostatistics
, vol.4
, pp. 249-264
-
-
Irizarry, R.A.1
Hobbs, B.2
Collin, F.3
Beazer-Barclay, Y.D.4
Antonellis, K.J.5
Scherf, U.6
Speed, T.P.7
-
31
-
-
77951294020
-
Resampling strategies for model assessment and selection
-
US: Springer
-
Simon R. Resampling strategies for model assessment and selection. In: Fundamentals of data mining in genomics and proteomics. US: Springer; 2007. p. 173–186.
-
(2007)
Fundamentals of data mining in genomics and proteomics
, pp. 173-186
-
-
Simon, R.1
-
32
-
-
84860796007
-
Resampling methods for meta-model validation with recommendations for evolutionary computation
-
Bischl B, Mersmann O, Trautmann H, Weihs C. Resampling methods for meta-model validation with recommendations for evolutionary computation. Evol Comput. 2012;20:249–275. doi: 10.1162/EVCO_a_00069
-
(2012)
Evol Comput
, vol.20
, pp. 249-275
-
-
Bischl, B.1
Mersmann, O.2
Trautmann, H.3
Weihs, C.4
-
33
-
-
84883216665
-
A package for survival analysis in S
-
Therneau TM. A package for survival analysis in S. R package version 2.37-4; 2013
-
(2013)
R package version
, vol.2
, pp. 34-37
-
-
Therneau, T.M.1
-
34
-
-
0003570192
-
Modeling survival data: extending the cox model
-
New York: Springer
-
Therneau TM,d Grambsch PM. Modeling survival data: extending the cox model. New York: Springer; 2000.
-
(2000)
-
-
Therneau, T.M.1
d Grambsch, P.M.2
-
35
-
-
79952934063
-
Regularization paths for Cox's proportional hazards model via coordinate descent
-
Simon N, Friedman J, Hastie T, Tibshirani R. Regularization paths for Cox's proportional hazards model via coordinate descent. J Statist Softw. 2011;39:1–13.
-
(2011)
J Statist Softw
, vol.39
, pp. 1-13
-
-
Simon, N.1
Friedman, J.2
Hastie, T.3
Tibshirani, R.4
-
36
-
-
84876100342
-
-
1/2012, TU Dortmund University, Available from
-
Bischl B, Lang M, Mersmann O, Rahnenführer J, Weihs C. Computing on high performance clusters with R: packages BatchJobs and BatchExperiments. 1/2012, TU Dortmund University, 2012. Available from: http://sfb876.tu-dortmund.de/PublicPublicationFiles/bischl_etal_2012a.pdf
-
(2012)
Computing on high performance clusters with R: packages BatchJobs and BatchExperiments
-
-
Bischl, B.1
Lang, M.2
Mersmann, O.3
Rahnenführer, J.4
Weihs, C.5
-
37
-
-
84877734926
-
Improving breast cancer survival analysis through competition-based multidimensional modeling
-
Bilal E, Dutkowski J, Guinney J, Jang IS, Logsdon BA, Pandey G, Sauerwine BA, Shimoni Y, Moen Vollan HK, Mecham BH, Rueda OM, Tost J, Curtis C, Alvarez MJ, Kristensen VN, Aparicio S, Børresen-Dale AL, Caldas C, Califano A, Friend SH, Ideker T, Schadt EE, Stolovitzky GA, Margolin AA. Improving breast cancer survival analysis through competition-based multidimensional modeling. PLoS Comput Biol. 2013;9.
-
(2013)
PLoS Comput Biol
, pp. 9
-
-
Bilal, E.1
Dutkowski, J.2
Guinney, J.3
Jang, I.S.4
Logsdon, B.A.5
Pandey, G.6
Sauerwine, B.A.7
Shimoni, Y.8
Moen Vollan, H.K.9
Mecham, B.H.10
Rueda, O.M.11
Tost, J.12
Curtis, C.13
Alvarez, M.J.14
Kristensen, V.N.15
Aparicio, S.16
Børresen-Dale, A.L.17
Caldas, C.18
Califano, A.19
Friend, S.H.20
Ideker, T.21
Schadt, E.E.22
Stolovitzky, G.A.23
Margolin, A.A.24
more..
-
38
-
-
84886475419
-
OpenML: a collaborative science platform
-
Blockeel H, Kersting K, Nijssen S, Železný F, (eds), Berlin Heidelberg: Springer
-
Rijn J, Bischl B, Torgo L, Gao B, Umaashankar V, Fischer S, Winter P, Wiswedel B, Berthold M, Vanschoren J. OpenML: a collaborative science platform. In: Blockeel H, Kersting K, Nijssen S, Železný F, editors, Machine learning and knowledge discovery in databases. Berlin Heidelberg: Springer; 2013. p. 645–649.
-
(2013)
Machine learning and knowledge discovery in databases
, pp. 645-649
-
-
Rijn, J.1
Bischl, B.2
Torgo, L.3
Gao, B.4
Umaashankar, V.5
Fischer, S.6
Winter, P.7
Wiswedel, B.8
Berthold, M.9
Vanschoren, J.10
-
39
-
-
84869201485
-
Practical Bayesian optimization of machine learning algorithms
-
Snoek J, Larochelle H, Adams R. Practical Bayesian optimization of machine learning algorithms. In: Pereira F, Burges C, Bottou L, Weinberger K, editors, Advances in neural information processing systems 25. 2012, p. 2960–2968
-
(2012)
Advances in neural information processing systems 25
, pp. 2960-2968
-
-
Snoek, J.1
Larochelle, H.2
Adams, R.3
Pereira, F.4
Burges, C.5
Bottou, L.6
Weinberger, K.7
|