Sample size planning for developing classifiers using high-dimensional DNA microarray data. - Wikidata - thesis-toulmin - TriplyDB

Sample size planning for developing classifiers using high-dimensional DNA microarray data.

Sample size planning for developing classifiers using high-dimensional DNA microarray data.

http://www.wikidata.org/entity/Q31036639

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Lessons learned in the analysis of high-dimensional data in vaccinomics Emerging concepts in biomarker discovery; the US-Japan Workshop on Immunological Molecular Markers in Oncology A simulation-approximation approach to sample size planning for high-dimensional classification studies.Preferred analysis methods for Affymetrix GeneChips. II. An expanded, balanced, wholly-defined spike-in dataset.Sample size considerations of prediction-validation methods in high-dimensional data for survival outcomes.Interpretation of genomic data: questions and answers.Factors influencing the statistical power of complex data analysis protocols for molecular signature development from microarray data Analysis of DNA microarray expression data Sample size and statistical power considerations in high-dimensionality data settings: a comparative study of classification algorithms Addressing the challenge of defining valid proteomic biomarkers and classifiers.Simulation of complex data structures for planning of studies with focus on biomarker comparison.Determination of sample size for a multi-class classifier based on single-nucleotide polymorphisms: a volume under the surface approach Using cross-validation to evaluate predictive accuracy of survival risk classifiers based on high-dimensional data.Optimally splitting cases for training and testing high dimensional classifiers Microarray-based cancer prediction using single genes Sample size planning for survival prediction with focus on high-dimensional data.Determination of minimum training sample size for microarray-based cancer outcome prediction-an empirical assessment.Improving the quality of biomarker discovery research: the right samples and enough of them Next generation sequencing profiling identifies miR-574-3p and miR-660-5p as potential novel prognostic markers for breast cancer.Cross-validation and Peeling Strategies for Survival Bump Hunting using Recursive Peeling Methods Profiling of Small Nucleolar RNAs by Next Generation Sequencing: Potential New Players for Breast Cancer Prognosis.R package PRIMsrc: Bump Hunting by Patient Rule Induction Method for Survival, Regression and Classification.Microarray-based expression profiling and informatics.Statistical aspect of translational and correlative studies in clinical trials.Design of the Nephrotic Syndrome Study Network (NEPTUNE) to evaluate primary glomerular nephropathy by a multidisciplinary approach.Genomic biomarkers for personalized medicine: development and validation in clinical studies Using microarrays to study the microenvironment in tumor biology: the crucial role of statistics.Impact of bioinformatic procedures in the development and translation of high-throughput molecular classifiers in oncology.Lost in translation: problems and pitfalls in translating laboratory observations to clinical utility.HOX expression patterns identify a common signature for favorable AML.A method for constructing a confidence bound for the actual error rate of a prediction rule in high dimensions.Adaptive clinical trial designs for simultaneous testing of matched diagnostics and therapeutics.Biomarkers for prostate cancer detection.Statistical issues in quality control of proteomic analyses: good experimental design and planning.Piwi-interacting RNAs and PIWI genes as novel prognostic markers for breast cancer Gene Expression Signatures for Head and Neck Cancer Patient Stratification: Are Results Ready for Clinical Application?Cross-Validation of Survival Bump Hunting by Recursive Peeling Methods.Validation of Transcriptomics-Based In Vitro Methods.Issues in developing multivariable molecular signatures for guiding clinical care decisions.Study design in high-dimensional classification analysis.

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P2860

Sample size planning for developing classifiers using high-dimensional DNA microarray data.

http://www.wikidata.org/entity/Q31036639

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2006 nî lūn-bûn

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2006 թուականի Ապրիլին հրատարակուած գիտական յօդուած

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2006 թվականի ապրիլին հրատարակված գիտական հոդված

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2006年の論文

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2006年論文

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2006年論文

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2006年論文

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2006年論文

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2006年論文

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2006年论文

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Sample size planning for devel ...... mensional DNA microarray data.

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Sample size planning for devel ...... mensional DNA microarray data.

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Sample size planning for devel ...... mensional DNA microarray data.

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Sample size planning for devel ...... mensional DNA microarray data.

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Sample size planning for devel ...... mensional DNA microarray data.

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Kevin K Dobbin

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Richard M Simon

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P2860

The use of molecular profiling to predict survival after chemotherapy for diffuse large-B-cell lymphoma.

Molecular classification of cancer: class discovery and class prediction by gene expression monitoring

A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer

Determination of minimum sample size and discriminatory expression patterns in microarray data.

Pitfalls in the use of DNA microarray data for diagnostic and prognostic classification.

Estimating dataset size requirements for classifying DNA microarray data.

Prediction error estimation: a comparison of resampling methods.

How many samples are needed to build a classifier: a general sequential approach.

Sample size determination in microarray experiments for class comparison and prognostic classification.

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101-117

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scholarly article

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10.1093/BIOSTATISTICS/KXJ036

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1

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8

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