Pitfalls in the Use of DNA Microarray Data for Diagnostic and Prognostic Classification

Simon, Richard; Radmacher, Michael D.; Dobbin, Kevin K.; McShane, Lisa M.

doi:10.1093/jnci/95.1.14

Cited by 930 publications

(653 citation statements)

References 22 publications

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“…The leave-one-out crossvalidation procedure provides a nearly unbiased estimate of the true error rate of the classification procedure. 38 Mean values, obtained averaging the results of all channels, were used to plot the ROC curves shown in Fig. 3.…”

Section: Resultsmentioning

confidence: 99%

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Analysis of MEG Background Activity in Alzheimer’s Disease Using Nonlinear Methods and ANFIS

et al. 2009

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Section: Resultsmentioning

confidence: 99%

“…Despite the fact that the accuracy decreases with this procedure, it provides a nearly unbiased estimate of the true error rate of the classification method. 38 INSERT …”

Section: Discussionmentioning

confidence: 99%

Analysis of MEG Background Activity in Alzheimer’s Disease Using Nonlinear Methods and ANFIS

et al. 2009

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“…These analyses followed and took into account recently published recommendations and notes of caution (Simon et al, 2003). The 303 individual spectra were divided into four sample groups ('classes'): HNSCC and healthy control mucosae were used as the training set; the additional tumoradjacent mucosae and tumor-distant mucosae were used as the test set.…”

Section: Supervised Class Comparison and Class Prediction Analysismentioning

confidence: 99%

Proteomic analysis reveals successive aberrations in protein expression from healthy mucosa to invasive head and neck cancer

et al. 2006

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“…Accordingly, we included all samples in the initial training set and the Kaplan-Meier plot was based on the results of a leave-oneout cross validation (LOOCV). The LOOCV provides a nearly unbiased estimate of the true error rate of the classification procedure [23]. At the end of the LOOCV process, we have constructed different models for each sample only to estimate the prediction error.…”

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confidence: 99%

Meta-analysis of gene expression profiles related to relapse-free survival in 1,079 breast cancer patients

Győrffy

Schäfer

2008

Breast Cancer Res Treat

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The transcriptome of breast cancers have been extensively screened with microarrays and large sets of genes associated with clinical features have been established. The aim of this study was to validate original gene sets on a large cohort of raw breast cancer microarray data with known clinical follow-up. We recovered 20 publications and matched them to Affymetrix HGU133A annotations. Raw Affymetrix HGU133A microarray data were extracted from GEO and MAS5 normalized. For classifying patients using the selected gene sets, we applied prediction analysis of microarrays and constructed KaplanMeier plots. A new classification including all patients was generated using supervised principal components analysis. Seven studies including 1,470 patients were downloaded from GEO. Notably, we uncovered 641 microarrays representing 251 individual tumor specimens among them, which were repeatedly described under independent GEO identifiers. We excluded all redundant data and used the remaining 1,079 samples. Eight of the 20 gene sets were able to predict response at a significance of P \ 0.05. The discrimination of good and poor prognosis groups exclusively relying on gene expression data resulted in high significance (P = 1.8E-12). A model including genes fitted by both gene expression and clinical covariates (lymph node status and grade) contains 44 genes and can predict response at P = 9.5E-7. The outcome provides a ranking of the gene lists regarding applicability on an independent dataset. We established a consensus predictor combining the available clinical and gene expression data. The database comprising expression profiles of 1,079 breast cancers can be used to classify individual patients.

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Pitfalls in the Use of DNA Microarray Data for Diagnostic and Prognostic Classification

Cited by 930 publications

References 22 publications

Analysis of MEG Background Activity in Alzheimer’s Disease Using Nonlinear Methods and ANFIS

Analysis of MEG Background Activity in Alzheimer’s Disease Using Nonlinear Methods and ANFIS

Proteomic analysis reveals successive aberrations in protein expression from healthy mucosa to invasive head and neck cancer

Meta-analysis of gene expression profiles related to relapse-free survival in 1,079 breast cancer patients

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