An efficient and robust statistical modeling approach to discover differentially expressed genes using genomic expression profiles.
about
How many replicates of arrays are required to detect gene expression changes in microarray experiments? A mixture model approach.The limit fold change model: a practical approach for selecting differentially expressed genes from microarray data.Assessment of differential gene expression in human peripheral nerve injury.A class of models for analyzing GeneChip gene expression analysis array dataTests for finding complex patterns of differential expression in cancers: towards individualized medicineEffect of sample size and P-value filtering techniques on the detection of transcriptional changes induced in rat neuroblastoma (NG108) cells by mefloquineA novel Mixture Model Method for identification of differentially expressed genes from DNA microarray dataRegulation of mouse hepatic genes in response to diet induced obesity, insulin resistance and fasting induced weight reduction.Feature selection and classification for microarray data analysis: evolutionary methods for identifying predictive genes.Design and analysis of a Petri net model of the Von Hippel-Lindau (VHL) tumor suppressor interaction networkRank products: a simple, yet powerful, new method to detect differentially regulated genes in replicated microarray experimentsIncorporating genome-scale tools for studying energy homeostasis.Individualized markers optimize class prediction of microarray dataGene expression profiling identifies genes predictive of oral squamous cell carcinoma.A genetic expression profile associated with oral cancer identifies a group of patients at high risk of poor survival.Integrative analysis of DNA copy number and gene expression in metastatic oral squamous cell carcinoma identifies genes associated with poor survivalCan a metastatic gene expression profile outperform tumor size as a predictor of occult lymph node metastasis in oral cancer patients?A method to identify differential expression profiles of time-course gene data with Fourier transformationGene features selection for three-class disease classification via multiple orthogonal partial least square discriminant analysis and S-plot using microarray dataA classification-based machine learning approach for the analysis of genome-wide expression dataComputational strategies for analyzing data in gene expression microarray experiments.Evaluation of nine strategies for analyzing a cDNA toxicology microarray data set.Using weighted permutation scores to detect differential gene expression with microarray data.Rank-based methods as a non-parametric alternative of the T-statistic for the analysis of biological microarray data.Comparison of various statistical methods for identifying differential gene expression in replicated microarray data.A two-sample Bayesian t-test for microarray data.ArraySolver: an algorithm for colour-coded graphical display and Wilcoxon signed-rank statistics for comparing microarray gene expression dataA comprehensive evaluation of SAM, the SAM R-package and a simple modification to improve its performancePattern-selection based power analysis and discrimination of low- and high-grade myelodysplastic syndromes study using SNP arrays.Estimating the false discovery rate using mixed normal distribution for identifying differentially expressed genes in microarray data analysis.Internal standard-based analysis of microarray data. Part 1: analysis of differential gene expressionsFunctional analysis: evaluation of response intensities--tailoring ANOVA for lists of expression subsetsDeciphering peripheral nerve myelination by using Schwann cell expression profiling.Supervised regularized canonical correlation analysis: integrating histologic and proteomic measurements for predicting biochemical recurrence following prostate surgeryFinding differentially expressed genes in high dimensional data: Rank based test statistic via a distance measure.Identifying dysregulated pathways in cancers from pathway interaction networks.A between-class overlapping filter-based method for transcriptome data analysis.Genomic DNA standards for gene expression profiling in Mycobacterium tuberculosis.Integrative genomics in combination with RNA interference identifies prognostic and functionally relevant gene targets for oral squamous cell carcinoma.Microarrays in ecology and evolution: a preview.
P2860
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P2860
An efficient and robust statistical modeling approach to discover differentially expressed genes using genomic expression profiles.
description
2001 nî lūn-bûn
@nan
2001 թուականի Յուլիսին հրատարակուած գիտական յօդուած
@hyw
2001 թվականի հուլիսին հրատարակված գիտական հոդված
@hy
2001年の論文
@ja
2001年論文
@yue
2001年論文
@zh-hant
2001年論文
@zh-hk
2001年論文
@zh-mo
2001年論文
@zh-tw
2001年论文
@wuu
name
An efficient and robust statis ...... g genomic expression profiles.
@ast
An efficient and robust statis ...... g genomic expression profiles.
@en
type
label
An efficient and robust statis ...... g genomic expression profiles.
@ast
An efficient and robust statis ...... g genomic expression profiles.
@en
prefLabel
An efficient and robust statis ...... g genomic expression profiles.
@ast
An efficient and robust statis ...... g genomic expression profiles.
@en
P2093
P2860
P356
P1433
P1476
An efficient and robust statis ...... g genomic expression profiles.
@en
P2093
J G Thomas
S J Tapscott
P2860
P304
P356
10.1101/GR.165101
P577
2001-07-01T00:00:00Z