메뉴 건너뛰기




Volumn 8, Issue 7, 2013, Pages

Determination of Minimum Training Sample Size for Microarray-Based Cancer Outcome Prediction-An Empirical Assessment

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; BREAST CANCER; CANCER SURVIVAL; CLASSIFIER; CONTROLLED STUDY; EVENT FREE SURVIVAL; GENE EXPRESSION; HUMAN; MALIGNANT NEOPLASTIC DISEASE; MICROARRAY ANALYSIS; MULTIPLE MYELOMA; NEUROBLASTOMA; OUTCOME ASSESSMENT; OVERALL SURVIVAL; PREDICTION; PROGNOSIS; QUALITY CONTROL; SAMPLE SIZE; SIGNAL NOISE RATIO; SUPPORT VECTOR MACHINE; TREATMENT RESPONSE;

EID: 84879812069     PISSN: None     EISSN: 19326203     Source Type: Journal    
DOI: 10.1371/journal.pone.0068579     Document Type: Article
Times cited : (11)

References (24)
  • 1
    • 74549174585 scopus 로고    scopus 로고
    • DNA Microarrays Are Predictive of Cancer Prognosis: A Re-evaluation
    • Fan XH, Shi LM, Fang H, Cheng YY, Perkins R, et al. (2010) DNA Microarrays Are Predictive of Cancer Prognosis: A Re-evaluation. Clin Cancer Res 16: 629-636.
    • (2010) Clin Cancer Res , vol.16 , pp. 629-636
    • Fan, X.H.1    Shi, L.M.2    Fang, H.3    Cheng, Y.Y.4    Perkins, R.5
  • 2
    • 84984932472 scopus 로고    scopus 로고
    • Exploring the new world of the genome with DNA microarrays
    • Brown PO, Botstein D, (1999) Exploring the new world of the genome with DNA microarrays. Nat Genet 21: 33-37.
    • (1999) Nat Genet , vol.21 , pp. 33-37
    • Brown, P.O.1    Botstein, D.2
  • 3
    • 0029852580 scopus 로고    scopus 로고
    • Use of a cDNA microarray to analyse gene expression patterns in human cancer
    • DeRisi J, Penland L, Brown PO, Bittner ML, Meltzer PS, et al. (1996) Use of a cDNA microarray to analyse gene expression patterns in human cancer. Nat Genet 14: 457-460.
    • (1996) Nat Genet , vol.14 , pp. 457-460
    • DeRisi, J.1    Penland, L.2    Brown, P.O.3    Bittner, M.L.4    Meltzer, P.S.5
  • 4
    • 2942729848 scopus 로고    scopus 로고
    • Gene expression profiles predict complete pathologic response to neoadjuvant paclitaxel and fluorouracil, doxorubicin, and cyclophosphamide chemotherapy in breast cancer
    • Ayers M, Symmans WF, Stec J, Damokosh AI, Clark E, et al. (2004) Gene expression profiles predict complete pathologic response to neoadjuvant paclitaxel and fluorouracil, doxorubicin, and cyclophosphamide chemotherapy in breast cancer. J Clin Oncol 22: 2284-2293.
    • (2004) J Clin Oncol , vol.22 , pp. 2284-2293
    • Ayers, M.1    Symmans, W.F.2    Stec, J.3    Damokosh, A.I.4    Clark, E.5
  • 6
    • 84879804581 scopus 로고    scopus 로고
    • The MicroArray Quality Control (MAQC)-II study of common practices for the development and validation of microarray-based predictive models
    • The MicroArray Quality Control Consortium
    • The MicroArray Quality Control Consortium (2010) The MicroArray Quality Control (MAQC)-II study of common practices for the development and validation of microarray-based predictive models. Pharmacogenomics J: S5-S16.
    • (2010) Pharmacogenomics J
  • 7
    • 0032518092 scopus 로고    scopus 로고
    • Using a stopping rule to determine the size of the training sample in a classification problem
    • Kundu S, Martinsek AT, (1998) Using a stopping rule to determine the size of the training sample in a classification problem. Stat Probab Lett 37: 19-27.
    • (1998) Stat Probab Lett , vol.37 , pp. 19-27
    • Kundu, S.1    Martinsek, A.T.2
  • 8
    • 0036738906 scopus 로고    scopus 로고
    • Determination of minimum sample size and discriminatory expression patterns in microarray data
    • Hwang DH, Schmitt WA, Stephanopoulos G, (2002) Determination of minimum sample size and discriminatory expression patterns in microarray data. Bioinformatics 18: 1184-1193.
    • (2002) Bioinformatics , vol.18 , pp. 1184-1193
    • Hwang, D.H.1    Schmitt, W.A.2    Stephanopoulos, G.3
  • 10
    • 12744260155 scopus 로고    scopus 로고
    • How many samples are needed to build a classifier: a general sequential approach
    • Fu WJJ, Dougherty ER, Mallick B, Carroll RJ, (2005) How many samples are needed to build a classifier: a general sequential approach. Bioinformatics 21: 63-70.
    • (2005) Bioinformatics , vol.21 , pp. 63-70
    • Fu, W.J.J.1    Dougherty, E.R.2    Mallick, B.3    Carroll, R.J.4
  • 11
    • 33845404310 scopus 로고    scopus 로고
    • Sample size planning for developing classifiers using high-dimensional DNA microarray data
    • Dobbin KK, Simon RM, (2007) Sample size planning for developing classifiers using high-dimensional DNA microarray data. Biostatistics 8: 101-117.
    • (2007) Biostatistics , vol.8 , pp. 101-117
    • Dobbin, K.K.1    Simon, R.M.2
  • 12
    • 70149093984 scopus 로고    scopus 로고
    • A simulation-approximation approach to sample size planning for high-dimensional classification studies
    • de Valpine P, Bitter HM, Brown MPS, Heller J, (2009) A simulation-approximation approach to sample size planning for high-dimensional classification studies. Biostatistics 10: 424-435.
    • (2009) Biostatistics , vol.10 , pp. 424-435
    • de Valpine, P.1    Bitter, H.M.2    Brown, M.P.S.3    Heller, J.4
  • 14
    • 67949103688 scopus 로고    scopus 로고
    • A weighted sample size for microarray datasets that considers the variability of variance and multiplicity
    • Kim KY, Chung HC, Rha SY, (2009) A weighted sample size for microarray datasets that considers the variability of variance and multiplicity. J Biosci Bioeng 108: 252-258.
    • (2009) J Biosci Bioeng , vol.108 , pp. 252-258
    • Kim, K.Y.1    Chung, H.C.2    Rha, S.Y.3
  • 15
    • 40749107034 scopus 로고    scopus 로고
    • How large a training set is needed to develop a classifier for microarray data?
    • Dobbin KK, Zhao Y, Simon RM, (2008) How large a training set is needed to develop a classifier for microarray data? Clin Cancer Res 14: 108-114.
    • (2008) Clin Cancer Res , vol.14 , pp. 108-114
    • Dobbin, K.K.1    Zhao, Y.2    Simon, R.M.3
  • 16
    • 26944473196 scopus 로고    scopus 로고
    • On the relationship between training sample size and data dimensionality: Monte Carlo analysis of broadband multi-temporal classification
    • Van Niel TG, McVicar TR, Datt B, (2005) On the relationship between training sample size and data dimensionality: Monte Carlo analysis of broadband multi-temporal classification. Remote Sens Environ 98: 468-480.
    • (2005) Remote Sens Environ , vol.98 , pp. 468-480
    • Van Niel, T.G.1    McVicar, T.R.2    Datt, B.3
  • 17
    • 77954168391 scopus 로고    scopus 로고
    • Effect of training-sample size and classification difficulty on the accuracy of genomic predictors
    • Popovici V, Chen WJ, Gallas BG, Hatzis C, Shi WW, et al. (2010) Effect of training-sample size and classification difficulty on the accuracy of genomic predictors. Breast Cancer Res 12.
    • (2010) Breast Cancer Res , vol.12
    • Popovici, V.1    Chen, W.J.2    Gallas, B.G.3    Hatzis, C.4    Shi, W.W.5
  • 18
    • 0037142053 scopus 로고    scopus 로고
    • The use of molecular profiling to predict survival after chemotherapy for diffuse large-B-cell lymphoma
    • Rosenwald A, Wright G, Chan WC, Connors JM, Campo E, et al. (2002) The use of molecular profiling to predict survival after chemotherapy for diffuse large-B-cell lymphoma. N Engl J Med 346: 1937-1947.
    • (2002) N Engl J Med , vol.346 , pp. 1937-1947
    • Rosenwald, A.1    Wright, G.2    Chan, W.C.3    Connors, J.M.4    Campo, E.5
  • 19
    • 13844310310 scopus 로고    scopus 로고
    • Gene-expression pro-files to predict distant metastasis of lymph-node-negative primary breast cancer
    • Wang YX, Klijn JGM, Zhang Y, Sieuwerts A, Look MP, et al. (2005) Gene-expression pro-files to predict distant metastasis of lymph-node-negative primary breast cancer. Lancet 365: 671-679.
    • (2005) Lancet , vol.365 , pp. 671-679
    • Wang, Y.X.1    Klijn, J.G.M.2    Zhang, Y.3    Sieuwerts, A.4    Look, M.P.5
  • 20
    • 77957935139 scopus 로고    scopus 로고
    • Genomic Index of Sensitivity to Endocrine Therapy for Breast Cancer
    • Symmans WF, Hatzis C, Sotiriou C, Andre F, Peintinger F, et al. (2010) Genomic Index of Sensitivity to Endocrine Therapy for Breast Cancer. J Clin Oncol 28: 4111-4119.
    • (2010) J Clin Oncol , vol.28 , pp. 4111-4119
    • Symmans, W.F.1    Hatzis, C.2    Sotiriou, C.3    Andre, F.4    Peintinger, F.5
  • 21
    • 0016772212 scopus 로고
    • Comparison of predicted and observed secondary structure of T4 phage lysozyme
    • Matthews BW, (1975) Comparison of predicted and observed secondary structure of T4 phage lysozyme. Biochim Biophys Acta 405: 442-451.
    • (1975) Biochim Biophys Acta , vol.405 , pp. 442-451
    • Matthews, B.W.1
  • 22
    • 0033569406 scopus 로고    scopus 로고
    • Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring
    • Golub TR, Slonim DK, Tamayo P, Huard C, Gaasenbeek M, 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    Slonim, D.K.2    Tamayo, P.3    Huard, C.4    Gaasenbeek, M.5
  • 24
    • 0036489046 scopus 로고    scopus 로고
    • Comparison of discrimination methods for the classification of tumors using gene expression data
    • Dudoit S, Fridlyand J, Speed TP, (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.P.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.