-
1
-
-
19944432091
-
A predictor based on the somatic genomic changes of the BRCA1/BRCA2 breast cancer tumors identifies the non-BRCA1/BRCA2 tumors with BRCA1 promoter hypermethylation
-
Alvarez, S., Diaz-Uriarte, R., Osorio, A., Barroso, A., Melchor, L., Paz, M. F., Honrado, E., Rodriguez, R., Urioste, M., Valle, L., Diez, O., Cigudosa, J. C., Dopazo, J., Esteller, M., and Benitez, J. (2005). A predictor based on the somatic genomic changes of the BRCA1/BRCA2 breast cancer tumors identifies the non-BRCA1/BRCA2 tumors with BRCA1 promoter hypermethylation. Clinical Cancer Research 11, 1146-1153.
-
(2005)
Clinical Cancer Research
, vol.11
, pp. 1146-1153
-
-
Alvarez, S.1
Diaz-Uriarte, R.2
Osorio, A.3
Barroso, A.4
Melchor, L.5
Paz, M.F.6
Honrado, E.7
Rodriguez, R.8
Urioste, M.9
Valle, L.10
Diez, O.11
Cigudosa, J.C.12
Dopazo, J.13
Esteller, M.14
Benitez, J.15
-
2
-
-
0037076322
-
Selection bias in gene extraction on the basis of microarray gene-expression data
-
Ambroise, C. and McLachlan, G. (2002). Selection bias in gene extraction on the basis of microarray gene-expression data. Proceedings of the National Academy of Sciences 99, 6562-6566.
-
(2002)
Proceedings of the National Academy of Sciences
, vol.99
, pp. 6562-6566
-
-
Ambroise, C.1
McLachlan, G.2
-
3
-
-
0036372855
-
New feature subset selection procedures for classification of expression profiles
-
RESEARCH0017
-
Bo, T. and Jonassen, I. (2002). New feature subset selection procedures for classification of expression profiles. Genome Biology 3, RESEARCH0017.
-
(2002)
Genome Biology
, vol.3
-
-
Bo, T.1
Jonassen, I.2
-
5
-
-
33846515112
-
Partial least squares: A versatile tool for the analysis of high-dimensional genomic data
-
Boulesteix, A.-L. and Strimmer, K. (2007). Partial least squares: a versatile tool for the analysis of high-dimensional genomic data. Briefings in Bioinformatics 8, 32-44.
-
(2007)
Briefings in Bioinformatics
, vol.8
, pp. 32-44
-
-
Boulesteix, A.-L.1
Strimmer, K.2
-
6
-
-
48249110665
-
Microarray-based classification and clinical predictors: On combined classifiers and additional predictive value
-
Boulesteix, A.-L., Porzelius, C., and Daumer, M. (2008). Microarray-based classification and clinical predictors: on combined classifiers and additional predictive value. Bioinformatics 24, 1698-1706.
-
(2008)
Bioinformatics
, vol.24
, pp. 1698-1706
-
-
Boulesteix, A.-L.1
Porzelius, C.2
Daumer, M.3
-
7
-
-
0030211964
-
Bagging predictors
-
Breiman, L. (1996). Bagging predictors. Machine Learning 26, 123-140.
-
(1996)
Machine Learning
, vol.26
, pp. 123-140
-
-
Breiman, L.1
-
9
-
-
0035478854
-
Random Forests
-
Breiman, L. (2001). Random Forests. Machine Learning 45, 5-32.
-
(2001)
Machine Learning
, vol.45
, pp. 5-32
-
-
Breiman, L.1
-
10
-
-
0003802343
-
-
Wadsworth, Belmont, CA
-
Breiman, L., Friedman, J. H., Olshen, R. A., and Stone, C. J. (1984). Classification and regression trees. Wadsworth, Belmont, CA.
-
(1984)
Classification and regression trees
-
-
Breiman, L.1
Friedman, J.H.2
Olshen, R.A.3
Stone, C.J.4
-
11
-
-
0034602774
-
Knowledge-based analysis of microarray gene expression data by using support vector machines
-
Brown, M., Grundy, W., Lin, D., Cristianini, N., Sugnet, C., Furey, T., Ares, M., and Haussler, D. (2000). Knowledge-based analysis of microarray gene expression data by using support vector machines. Proceedings of the National Academy of Sciences 97, 262-267.
-
(2000)
Proceedings of the National Academy of Sciences
, vol.97
, pp. 262-267
-
-
Brown, M.1
Grundy, W.2
Lin, D.3
Cristianini, N.4
Sugnet, C.5
Furey, T.6
Ares, M.7
Haussler, D.8
-
12
-
-
41549141939
-
Boosting algorithms: Regularization, prediction and model fitting
-
With discussion
-
Bühlmann, P. and Hothorn, T. (2007). Boosting algorithms: regularization, prediction and model fitting. Statistical Science 22, 477-505. With discussion.
-
(2007)
Statistical Science
, vol.22
, pp. 477-505
-
-
Bühlmann, P.1
Hothorn, T.2
-
14
-
-
85153200676
-
-
Bureau, A., Dupuis, J., Falls, K., Lunetta, K. L., Hayward, B., Keith, T. P., and van Eerdewegh, P. (2005). Identifying SNPs predictive of phenotype using Random Forests. Genetic Epidemiology 28, 171-1 82.
-
Bureau, A., Dupuis, J., Falls, K., Lunetta, K. L., Hayward, B., Keith, T. P., and van Eerdewegh, P. (2005). Identifying SNPs predictive of phenotype using Random Forests. Genetic Epidemiology 28, 171-1 82.
-
-
-
-
15
-
-
3342991601
-
Gene selection and classification from microarray data using kernel machine
-
Cho, J. H., Lee, D., Park, J. H., and Lee, I. B. (2004). Gene selection and classification from microarray data using kernel machine. FEBS Letters 571, 93-98.
-
(2004)
FEBS Letters
, vol.571
, pp. 93-98
-
-
Cho, J.H.1
Lee, D.2
Park, J.H.3
Lee, I.B.4
-
16
-
-
0036155238
-
A perspective on epistasis: Limits of models displaying no main effect
-
Culverhouse, R., Suarez, B. K., Lin, J., and Reich, T. (2002). A perspective on epistasis: limits of models displaying no main effect. American Journal of Human Genetics 70,461-471.
-
(2002)
American Journal of Human Genetics
, vol.70
, pp. 461-471
-
-
Culverhouse, R.1
Suarez, B.K.2
Lin, J.3
Reich, T.4
-
17
-
-
12344294601
-
BagBoosting for tumor classification with gene expression data
-
Dettling, M. (2004). BagBoosting for tumor classification with gene expression data. Bioinformatics 20,3583-3593.
-
(2004)
Bioinformatics
, vol.20
, pp. 3583-3593
-
-
Dettling, M.1
-
18
-
-
0038391397
-
Boosting for tumor classification with gene expression data
-
Dettling, M. and Bühlmann (2003). Boosting for tumor classification with gene expression data. Bioinformatics 19, 1061-1069.
-
(2003)
Bioinformatics
, vol.19
, pp. 1061-1069
-
-
Dettling, M.1
Bühlmann2
-
19
-
-
30644464444
-
Gene selection and classification of microarray data using Random Forest
-
Diaz-Uriarte, R. and Alvarez de Andres, S. (2006). Gene selection and classification of microarray data using Random Forest. BMC Bioinformatics 7, 3.
-
(2006)
BMC Bioinformatics
, vol.7
, pp. 3
-
-
Diaz-Uriarte, R.1
Alvarez de Andres, S.2
-
20
-
-
17644384367
-
Minimum redundancy feature selection from microarray gene expression data
-
Ding, C. and Peng, H. (2005). Minimum redundancy feature selection from microarray gene expression data. Journal of Bioinformatics and Computational Biology 3, 185-205.
-
(2005)
Journal of Bioinformatics and Computational Biology
, vol.3
, pp. 185-205
-
-
Ding, C.1
Peng, H.2
-
21
-
-
0038391443
-
Bagging to improve the accuracy ofa clustering procedure
-
Dudoit, S. and Fridlyand, J. (2003). Bagging to improve the accuracy ofa clustering procedure. Bioinformatics 19, 1090-1099.
-
(2003)
Bioinformatics
, vol.19
, pp. 1090-1099
-
-
Dudoit, S.1
Fridlyand, J.2
-
23
-
-
0036489046
-
Comparison of discrimination methods for the classification of tumors using gene expression data
-
Dudoit, S., Fridlyand, J., and Speed, T. (2002). Comparison of discrimination methods for the classification of tumors using gene expression data. Journal of the American Statistical Association 97, 77-87.
-
(2002)
Journal of the American Statistical Association
, vol.97
, pp. 77-87
-
-
Dudoit, S.1
Fridlyand, J.2
Speed, T.3
-
24
-
-
0043203327
-
Multiple hypothesis testing in microarray experiments
-
Dudoit, S., Shaffer, J. P., and Boldrick, J. C. (2003). Multiple hypothesis testing in microarray experiments. Statistical Science 18,71-103.
-
(2003)
Statistical Science
, vol.18
, pp. 71-103
-
-
Dudoit, S.1
Shaffer, J.P.2
Boldrick, J.C.3
-
25
-
-
0034863834
-
Classification of microarray data with penalized logistic regression
-
Bittner, M. L, Chen, Y, Dorsel, A. N, and Dougherty, E. R, Eds
-
Eilers, P. H., Boer, J. M., van Ommen, G. J., and van Houwelingen, H. C. (2001). Classification of microarray data with penalized logistic regression. In Bittner, M. L., Chen, Y., Dorsel, A. N., and Dougherty, E. R. (Eds.), Proceedings of SPIE, volume 4266, pp. 187-198.
-
(2001)
Proceedings of SPIE
, vol.4266
, pp. 187-198
-
-
Eilers, P.H.1
Boer, J.M.2
van Ommen, G.J.3
van Houwelingen, H.C.4
-
26
-
-
0003909532
-
Discriminatory analysis - nonparametric discrimination: Consistency properties
-
Technical report, USAF School ofAviation Medicine
-
Fix, E. and Hodges, J. L. (1951). Discriminatory analysis - nonparametric discrimination: consistency properties. Technical report, USAF School ofAviation Medicine.
-
(1951)
-
-
Fix, E.1
Hodges, J.L.2
-
27
-
-
0002978642
-
Experiments with a new boosting algorithm
-
Morgan Kauffman, San Francisco, CA, pp
-
Freund, Y. and Shapire, R. E. (1996). Experiments with a new boosting algorithm. In Machine Learning: Proceedings of the 13th International Conference. Morgan Kauffman, San Francisco, CA, pp. 148-156.
-
(1996)
Machine Learning: Proceedings of the 13th International Conference
, pp. 148-156
-
-
Freund, Y.1
Shapire, R.E.2
-
29
-
-
0034164230
-
Additive logistic regression: A statistical view of boosting
-
With discussion
-
Friedman, J., Hastie, T., and Tibshirani, R. (2000). Additive logistic regression: a statistical view of boosting. The Annals of Statistics 28, 337-407. With discussion.
-
(2000)
The Annals of Statistics
, vol.28
, pp. 337-407
-
-
Friedman, J.1
Hastie, T.2
Tibshirani, R.3
-
30
-
-
0033636139
-
Support vector machine classification and validation of cancer tissue samples using microarray expression data
-
Furey, T. S., Cristianini, N., Duffy, N., Bednarski, D. W., Schummer, M., and Haussler, D. (2000). Support vector machine classification and validation of cancer tissue samples using microarray expression data. Bioinformatics 16, 906-914.
-
(2000)
Bioinformatics
, vol.16
, pp. 906-914
-
-
Furey, T.S.1
Cristianini, N.2
Duffy, N.3
Bednarski, D.W.4
Schummer, M.5
Haussler, D.6
-
31
-
-
2942602999
-
Entropy-based gene ranking without selection bias for the predictive classification ofmicroarray data
-
Furlanello, C., Serafini, M., Merler, S., and Jurman, G. (2003). Entropy-based gene ranking without selection bias for the predictive classification ofmicroarray data. BMC Bioinformatics 4, 54.
-
(2003)
BMC Bioinformatics
, vol.4
, pp. 54
-
-
Furlanello, C.1
Serafini, M.2
Merler, S.3
Jurman, G.4
-
32
-
-
0041421151
-
Prediction of clinical drug efficacy by classification of drug-induced genomic expression profiles in vitro
-
Günther, E., Stone, D. J., Gerwien, R. W., Bento, P., and Heyes, M. P. (2003). Prediction of clinical drug efficacy by classification of drug-induced genomic expression profiles in vitro. Proceedings of the National Academy of Sciences 100, 9608-9613.
-
(2003)
Proceedings of the National Academy of Sciences
, vol.100
, pp. 9608-9613
-
-
Günther, E.1
Stone, D.J.2
Gerwien, R.W.3
Bento, P.4
Heyes, M.P.5
-
33
-
-
33845413755
-
Regularized linear discriminant analysis and its application in microarrays
-
Guo, Y., Hastie, T., and Tibshirani, R. (2007). Regularized linear discriminant analysis and its application in microarrays. Biostatistics 8, 86-100.
-
(2007)
Biostatistics
, vol.8
, pp. 86-100
-
-
Guo, Y.1
Hastie, T.2
Tibshirani, R.3
-
34
-
-
0036161259
-
Gene selection for cancer classification using support vector machines
-
Guyon, I., Weston, J., Bamhill, S., and Vapnik, V. (2002). Gene selection for cancer classification using support vector machines. Machine Learning 46, 389-422.
-
(2002)
Machine Learning
, vol.46
, pp. 389-422
-
-
Guyon, I.1
Weston, J.2
Bamhill, S.3
Vapnik, V.4
-
35
-
-
85153291978
-
-
Guyon, I, Gunn, S, Nikravesh, M, and Zadeh, L. A, Eds, Springer, Heidelberg
-
Guyon, I., Gunn, S., Nikravesh, M., and Zadeh, L. A. (Eds.) (2006). Feature Extra ction. Foundations and Applications. Springer, Heidelberg.
-
(2006)
Feature Extra ction. Foundations and Applications
-
-
-
36
-
-
0003684449
-
-
Springer, New York, NY
-
Hastie, T., Tibshirani, R., and Friedman, J. H. (2001). The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer, New York, NY
-
(2001)
The Elements of Statistical Learning: Data Mining, Inference, and Prediction
-
-
Hastie, T.1
Tibshirani, R.2
Friedman, J.H.3
-
37
-
-
84942484786
-
Ridge regression: Biased estimation for nonorthogonal problems
-
Hoerl, A. E. and Kennard, R. (1970). Ridge regression: biased estimation for nonorthogonal problems. Technometrics 12, 55-67.
-
(1970)
Technometrics
, vol.12
, pp. 55-67
-
-
Hoerl, A.E.1
Kennard, R.2
-
38
-
-
0037976550
-
Improved gene selection for classification ofmicroarrays
-
Jaeger, J., Sengupta, R., and Ruzzo, W. L. (2003). Improved gene selection for classification ofmicroarrays. Pacific Symposium on Biocomputing 8, 53-64.
-
(2003)
Pacific Symposium on Biocomputing
, vol.8
, pp. 53-64
-
-
Jaeger, J.1
Sengupta, R.2
Ruzzo, W.L.3
-
39
-
-
19344366580
-
Early diagnostic marker panel determination for microarray based clinical studies
-
Article
-
Jaeger, J., Weichenhan, D., Ivandic, B., and Spang, R. (2005). Early diagnostic marker panel determination for microarray based clinical studies. Statistical Applications in Genetics and Molecular Biology 4, Article 9.
-
(2005)
Statistical Applications in Genetics and Molecular Biology
, vol.4
, pp. 9
-
-
Jaeger, J.1
Weichenhan, D.2
Ivandic, B.3
Spang, R.4
-
40
-
-
26444479778
-
Optimization by simulated annealing
-
Kirkpatrick, S., Gelatt, C. D. J., and Vecchi, M. P. (1983). Optimization by simulated annealing. Science 220, 671-680.
-
(1983)
Science
, vol.220
, pp. 671-680
-
-
Kirkpatrick, S.1
Gelatt, C.D.J.2
Vecchi, M.P.3
-
41
-
-
0034796896
-
Sequence analysis using logic regression
-
Kooperberg, C., Ruczinski, I., LeBlanc, M., and Hsu, L. (2001). Sequence analysis using logic regression. Genetic Epidemiology 21, 626-631.
-
(2001)
Genetic Epidemiology
, vol.21
, pp. 626-631
-
-
Kooperberg, C.1
Ruczinski, I.2
LeBlanc, M.3
Hsu, L.4
-
42
-
-
10444280144
-
An extensive comparison of recent classification tools applied to microarray data
-
Lee, J. W., Lee, J. B., Park, M., and Song, S. H. (2005). An extensive comparison of recent classification tools applied to microarray data. Computational Statistics & Data Analysis 48, 869-885.
-
(2005)
Computational Statistics & Data Analysis
, vol.48
, pp. 869-885
-
-
Lee, J.W.1
Lee, J.B.2
Park, M.3
Song, S.H.4
-
43
-
-
0038729565
-
Classification of multiple cancer types by multicategory support vector machines using gene expression data
-
Lee, Y and Lee, C. K. (2003). Classification of multiple cancer types by multicategory support vector machines using gene expression data. Bioinformatics 19, 1132-1139.
-
(2003)
Bioinformatics
, vol.19
, pp. 1132-1139
-
-
Lee, Y.1
Lee, C.K.2
-
44
-
-
25944479013
-
Bagged clustering
-
Technical Report 51, Vienna University of Economics and Business Administration, Vienna, Austria
-
Leisch, F. (1999). Bagged clustering. Technical Report 51, Vienna University of Economics and Business Administration, Vienna, Austria.
-
(1999)
-
-
Leisch, F.1
-
45
-
-
33847007697
-
Sparse logistic regression with Lp penalty for biomarker identification
-
Article
-
Liu, Z., Jiang, F., Tian, G., Wang, S., Sato, F., Meltzer, S., and Tan, M. (2007). Sparse logistic regression with Lp penalty for biomarker identification. Statistical Applications in Genetics and Molecular Biology 6, Article 6.
-
(2007)
Statistical Applications in Genetics and Molecular Biology
, vol.6
, pp. 6
-
-
Liu, Z.1
Jiang, F.2
Tian, G.3
Wang, S.4
Sato, F.5
Meltzer, S.6
Tan, M.7
-
46
-
-
85153317760
-
Analysis of expression data: Classification of patients
-
Lengauer, T, Ed, Wiley-VCH, Weinheim, chapter 28, pp
-
Lottaz, C., Kostka, D., and Spang, R. (2007). Analysis of expression data: classification of patients. In Lengauer, T. (Ed.), Bioinformatics - From Genomes to Therapies, 2. Getting at the Inner Workings: Molecular Interactions. Wiley-VCH, Weinheim, chapter 28, pp. 1023-1059.
-
(2007)
Bioinformatics - From Genomes to Therapies, 2. Getting at the Inner Workings: Molecular Interactions
, pp. 1023-1059
-
-
Lottaz, C.1
Kostka, D.2
Spang, R.3
-
47
-
-
25444453244
-
Screening large-scale association study data: Exploiting interactions using Random Forests
-
Lunetta, K. L., Hayward, L., Segal, J., and van Eerdewegh, P. (2004). Screening large-scale association study data: exploiting interactions using Random Forests. BMC Genetics 5, 32.
-
(2004)
BMC Genetics
, vol.5
, pp. 32
-
-
Lunetta, K.L.1
Hayward, L.2
Segal, J.3
van Eerdewegh, P.4
-
48
-
-
9244261068
-
Evaluating methods for classifying expression data
-
1065-1 084
-
Man, M. Z., Dyson, G., Johnson, K., and Liao, B. (2004). Evaluating methods for classifying expression data. Journal of Biopharmaceutical Statistics 14, 1065-1 084.
-
(2004)
Journal of Biopharmaceutical Statistics
, vol.14
-
-
Man, M.Z.1
Dyson, G.2
Johnson, K.3
Liao, B.4
-
49
-
-
22944466045
-
Molecular diagnosis, classification, model selection and performance evaluation
-
Markowetz, F. and Spang, R. (2005). Molecular diagnosis, classification, model selection and performance evaluation. Methods of Information in Medicine 44, 438-443.
-
(2005)
Methods of Information in Medicine
, vol.44
, pp. 438-443
-
-
Markowetz, F.1
Spang, R.2
-
50
-
-
36949021059
-
Detecting high-order interactions of single nucleotide polymorphisms using genetic programming
-
Nunkesser, R., Bernholt, T., Schwender, H., Ickstadt, K., and Wegener, I. (2007). Detecting high-order interactions of single nucleotide polymorphisms using genetic programming. Bioinformatics 23, 3280- 3288.
-
(2007)
Bioinformatics
, vol.23
, pp. 3280-3288
-
-
Nunkesser, R.1
Bernholt, T.2
Schwender, H.3
Ickstadt, K.4
Wegener, I.5
-
51
-
-
37249080278
-
Penalized logistic regression for detecting gene interactions
-
Park, M. Y. and Hastie, T. (2008). Penalized logistic regression for detecting gene interactions. Biostatistics 9, 30-50.
-
(2008)
Biostatistics
, vol.9
, pp. 30-50
-
-
Park, M.Y.1
Hastie, T.2
-
53
-
-
84863304598
-
-
R Development Core Team , R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0
-
R Development Core Team (2008). R: A Language and Environmentfor Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0.
-
(2008)
R: A Language and Environmentfor Statistical Computing
-
-
-
55
-
-
0347201147
-
Multiclass cancer diagnosis using tumor gene expression signatures
-
Ramaswamy, S., Tamayo, P., Rifkin, R., Mukherjee, S., Yeang, C. H., Angelo, M., Ladd, C., Reich, M., Latulippe, E., Mesirov, J. P., Poggio, T., Gerald, W., Loda, M., Lander, E. S., and Golub, T. R. (2001). Multiclass cancer diagnosis using tumor gene expression signatures. Proceedings of the National Academy of Sciences 98, 15149-15154.
-
(2001)
Proceedings of the National Academy of Sciences
, vol.98
, pp. 15149-15154
-
-
Ramaswamy, S.1
Tamayo, P.2
Rifkin, R.3
Mukherjee, S.4
Yeang, C.H.5
Angelo, M.6
Ladd, C.7
Reich, M.8
Latulippe, E.9
Mesirov, J.P.10
Poggio, T.11
Gerald, W.12
Loda, M.13
Lander, E.S.14
Golub, T.R.15
-
57
-
-
0141872478
-
Logic regression
-
Ruczinski, I., Kooperberg, C., and LeBlanc, M. (2003). Logic regression. Journal of Computational and Graphical Statistics 12, 475-511.
-
(2003)
Journal of Computational and Graphical Statistics
, vol.12
, pp. 475-511
-
-
Ruczinski, I.1
Kooperberg, C.2
LeBlanc, M.3
-
58
-
-
12744255343
-
Exploring interactions in high-dimensional genomic data: An overview of logic regression, with applications
-
Ruczinski, I., Kooperberg, C., and LeBlanc, M. (2004). Exploring interactions in high-dimensional genomic data: an overview of logic regression, with applications. Journal of Multivariate Analysis 90, 178-195.
-
(2004)
Journal of Multivariate Analysis
, vol.90
, pp. 178-195
-
-
Ruczinski, I.1
Kooperberg, C.2
LeBlanc, M.3
-
59
-
-
18944393902
-
A compendium to ensure computational reproducibility in high-dimensional classification tasks
-
Article
-
Ruschhaupt, M., Huber, W., Poustka, A., and Mansmann, U. (2004). A compendium to ensure computational reproducibility in high-dimensional classification tasks. Statistical Applications in Genetics and Molecular Biology 3, Article 37.
-
(2004)
Statistical Applications in Genetics and Molecular Biology
, vol.3
, pp. 37
-
-
Ruschhaupt, M.1
Huber, W.2
Poustka, A.3
Mansmann, U.4
-
61
-
-
38049070957
-
Picking single-nucleotide polymorphisms in forests
-
Schwarz, D., Szymczak, S., Ziegler, A., and Knig, I. R. (2007). Picking single-nucleotide polymorphisms in forests. BMC Proceedings 1(Suppl 1), S59.
-
(2007)
BMC Proceedings
, vol.1
, Issue.SUPPL. 1
-
-
Schwarz, D.1
Szymczak, S.2
Ziegler, A.3
Knig, I.R.4
-
62
-
-
37249085147
-
Identification of SNP interactions using logic regression
-
Schwender, H. and Ickstadt, K. (2008). Identification of SNP interactions using logic regression. Biostatistics 9, 187-198.
-
(2008)
Biostatistics
, vol.9
, pp. 187-198
-
-
Schwender, H.1
Ickstadt, K.2
-
63
-
-
2942612346
-
A pilot study on the application of statistical classification procedures to molecular epidemiological data
-
Schwender, H., Zucknick, M., Ickstadt, K., Bolt, H. M., and Network, G. (2004). A pilot study on the application of statistical classification procedures to molecular epidemiological data. Toxicology Letters 151, 29 1-299.
-
(2004)
Toxicology Letters
, vol.151
, Issue.29
, pp. 1-299
-
-
Schwender, H.1
Zucknick, M.2
Ickstadt, K.3
Bolt, H.M.4
Network, G.5
-
64
-
-
57649213123
-
Do you speak Genomish?
-
Schwender, H., Rabstein, S., and Ickstadt, K. (2006). Do you speak Genomish? Chance 19, 3-10.
-
(2006)
Chance
, vol.19
, pp. 3-10
-
-
Schwender, H.1
Rabstein, S.2
Ickstadt, K.3
-
65
-
-
0345327592
-
A simple and efficient algorithm for gene selection using sparse logistic regression
-
Shevade, S. K. and Keerthi, S. S. (2003). A simple and efficient algorithm for gene selection using sparse logistic regression. Bioinformatics 19, 2246-2253.
-
(2003)
Bioinformatics
, vol.19
, pp. 2246-2253
-
-
Shevade, S.K.1
Keerthi, S.S.2
-
66
-
-
0037245343
-
Pitfalls in the use of DNA microarray data for diagnostic and prognostic classification
-
Simon, R., Radmacher, M. D., Dobbin, K., and McShane, L. M. (2003). Pitfalls in the use of DNA microarray data for diagnostic and prognostic classification. Journal of the National Cancer Institute 95, 14-18.
-
(2003)
Journal of the National Cancer Institute
, vol.95
, pp. 14-18
-
-
Simon, R.1
Radmacher, M.D.2
Dobbin, K.3
McShane, L.M.4
-
67
-
-
33847096395
-
Bias in Random Forest variable importance measures: Illustrations, sources and a solution
-
Strobl, C., Boulesteix, A. L., Zeileis, A., and Hothorn, T. (2007). Bias in Random Forest variable importance measures: illustrations, sources and a solution. BMC Bioinformatics 8, 25.
-
(2007)
BMC Bioinformatics
, vol.8
, pp. 25
-
-
Strobl, C.1
Boulesteix, A.L.2
Zeileis, A.3
Hothorn, T.4
-
68
-
-
2942596534
-
Ensemble machine learning on gene expression data for cancer classification
-
Tan, A. C. and Gilbert, D. (2003). Ensemble machine learning on gene expression data for cancer classification. Applied Bioinformatics 2(Suppl 3), S75-S83.
-
(2003)
Applied Bioinformatics
, vol.2
, Issue.SUPPL. 3
-
-
Tan, A.C.1
Gilbert, D.2
-
69
-
-
0036141951
-
Finding genes in the c2c12 osteogenic pathway by k-nearest-neighbor classification of expression data
-
Theilhaber, J., Connolly, T., Roman-Roman, S., Bushnell, S., Jackson, A., Call, K., Garcia, T., and Baron, R. (2002). Finding genes in the c2c12 osteogenic pathway by k-nearest-neighbor classification of expression data. Genome Research 12, 165-176.
-
(2002)
Genome Research
, vol.12
, pp. 165-176
-
-
Theilhaber, J.1
Connolly, T.2
Roman-Roman, S.3
Bushnell, S.4
Jackson, A.5
Call, K.6
Garcia, T.7
Baron, R.8
-
71
-
-
2342533421
-
Class prediction by nearest shrunken centroids, with applications to DNA microarray s
-
Tibshirani, R., Hastie, T., Narasimhan, B., and Chu, G. (2003). Class prediction by nearest shrunken centroids, with applications to DNA microarray s. Statistical Science 18, 104-117.
-
(2003)
Statistical Science
, vol.18
, pp. 104-117
-
-
Tibshirani, R.1
Hastie, T.2
Narasimhan, B.3
Chu, G.4
-
72
-
-
0035942271
-
Significance analysis ofmicroarrays applied to the ionizing radiation response
-
Tusher, V., Tibshirani, R., and Chu, G. (2001). Significance analysis ofmicroarrays applied to the ionizing radiation response. Proceedings of the National Academy of Sciences 98, 5116-5124.
-
(2001)
Proceedings of the National Academy of Sciences
, vol.98
, pp. 5116-5124
-
-
Tusher, V.1
Tibshirani, R.2
Chu, G.3
-
74
-
-
0034796714
-
Introduction: Analysis of sequence data and population structure
-
Witte, J. S. and Fijal, B. A. (2001). Introduction: analysis of sequence data and population structure. Genetic Epidemiology 21, 600-601.
-
(2001)
Genetic Epidemiology
, vol.21
, pp. 600-601
-
-
Witte, J.S.1
Fijal, B.A.2
-
75
-
-
33947313355
-
A regression-based k nearest neighbor algorithm for gene function prediction from heterogeneous data
-
Yao, Z. and Ruzzo, W. L. (2006). A regression-based k nearest neighbor algorithm for gene function prediction from heterogeneous data. BMC Bioinformatics 7(Suppl 1), S11.
-
(2006)
BMC Bioinformatics
, vol.7
, Issue.SUPPL. 1
-
-
Yao, Z.1
Ruzzo, W.L.2
-
76
-
-
15944363312
-
Classification of gene microarrays by penalized logistic regression
-
Zhu, J. and Hastie, T. (2004). Classification of gene microarrays by penalized logistic regression. Biostatistics 5, 427-443.
-
(2004)
Biostatistics
, vol.5
, pp. 427-443
-
-
Zhu, J.1
Hastie, T.2
-
77
-
-
41149152918
-
Biostatistical aspects of genome-wide association studies
-
Ziegler, A., König, I. R., and Thompson, J. R. (2008). Biostatistical aspects of genome-wide association studies. Biometrical Journal 50, 8-28.
-
(2008)
Biometrical Journal
, vol.50
, pp. 8-28
-
-
Ziegler, A.1
König, I.R.2
Thompson, J.R.3
|