How large a training set is needed to develop a classifier for microarray data?
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Clinical outcome prediction by microRNAs in human cancer: a systematic reviewLessons learned in the analysis of high-dimensional data in vaccinomicsRNA expression patterns in serum microvesicles from patients with glioblastoma multiforme and controlsIntegrative computational biology for cancer researchEmerging concepts in biomarker discovery; the US-Japan Workshop on Immunological Molecular Markers in OncologyHead and neck cancer subtypes with biological and clinical relevance: Meta-analysis of gene-expression data.Interpretation of genomic data: questions and answers.Factors influencing the statistical power of complex data analysis protocols for molecular signature development from microarray dataAssessing the human immune system through blood transcriptomics.Module-based prediction approach for robust inter-study predictions in microarray data.Addressing the challenge of defining valid proteomic biomarkers and classifiers.Determination of sample size for a multi-class classifier based on single-nucleotide polymorphisms: a volume under the surface approachMolecular sub-classification of renal epithelial tumors using meta-analysis of gene expression microarrays.Microarray-based cancer prediction using single genesMicro RNA expression profiles as adjunctive data to assess the risk of hepatocellular carcinoma recurrence after liver transplantation.Predicting sample size required for classification performance.Effects of sample size on differential gene expression, rank order and prediction accuracy of a gene signatureDetermination of minimum training sample size for microarray-based cancer outcome prediction-an empirical assessment.Discrimination of normal and esophageal cancer plasma proteomes by MALDI-TOF mass spectrometry.Next generation sequencing profiling identifies miR-574-3p and miR-660-5p as potential novel prognostic markers for breast cancer.Risk prediction for late-stage ovarian cancer by meta-analysis of 1525 patient samples.Profiling of Small Nucleolar RNAs by Next Generation Sequencing: Potential New Players for Breast Cancer Prognosis.A decade of genome-wide gene expression profiling in acute myeloid leukemia: flashback and prospects.A prototype tobacco-associated oral squamous cell carcinoma classifier using RNA from brush cytologyLost in translation: problems and pitfalls in translating laboratory observations to clinical utility.Effects of sample size on robustness and prediction accuracy of a prognostic gene signature.Integrative genomic and transcriptomic characterization of papillary carcinomas of the breast.Investigation of radiosensitivity gene signatures in cancer cell lines.Investigating MicroRNA Expression Profiles in Pancreatic Cystic Neoplasms.Piwi-interacting RNAs and PIWI genes as novel prognostic markers for breast cancerPrediction of outcome in internet-delivered cognitive behaviour therapy for paediatric obsessive-compulsive disorder: A machine learning approach.Identification and Clinical Translation of Biomarker Signatures: Statistical Considerations.Genomic analyses to select patients for adjuvant chemotherapy: trials and tribulations.Experimental Designs on High-Throughput Biological ExperimentsUse of genome-wide high-throughput technologies in biomarker development
P2860
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P2860
How large a training set is needed to develop a classifier for microarray data?
description
2008 nî lūn-bûn
@nan
2008 թուականի Յունուարին հրատարակուած գիտական յօդուած
@hyw
2008 թվականի հունվարին հրատարակված գիտական հոդված
@hy
2008年の論文
@ja
2008年論文
@yue
2008年論文
@zh-hant
2008年論文
@zh-hk
2008年論文
@zh-mo
2008年論文
@zh-tw
2008年论文
@wuu
name
How large a training set is needed to develop a classifier for microarray data?
@ast
How large a training set is needed to develop a classifier for microarray data?
@en
type
label
How large a training set is needed to develop a classifier for microarray data?
@ast
How large a training set is needed to develop a classifier for microarray data?
@en
prefLabel
How large a training set is needed to develop a classifier for microarray data?
@ast
How large a training set is needed to develop a classifier for microarray data?
@en
P2093
P1476
How large a training set is needed to develop a classifier for microarray data?
@en
P2093
Kevin K Dobbin
Richard M Simon
Yingdong Zhao
P304
P356
10.1158/1078-0432.CCR-07-0443
P407
P577
2008-01-01T00:00:00Z