Linking gene expression data with patient survival times using partial least squares.
about
Multi-class cancer classification by total principal component regression (TPCR) using microarray gene expression dataSparse partial least-squares regression for high-throughput survival data analysis.A framework for significance analysis of gene expression data using dimension reduction methods.Computational strategies for analyzing data in gene expression microarray experiments.Penalized Cox regression analysis in the high-dimensional and low-sample size settings, with applications to microarray gene expression data.Use of extreme patient samples for outcome prediction from gene expression data.Outcome-Driven Cluster Analysis with Application to Microarray Data.Additive risk models for survival data with high-dimensional covariates.A multivariate approach for integrating genome-wide expression data and biological knowledgePredicting patient survival from microarray data by accelerated failure time modeling using partial least squares and LASSO.Predicting survival from microarray data--a comparative study.Partial least squares Cox regression for genome-wide data.SignS: a parallelized, open-source, freely available, web-based tool for gene selection and molecular signatures for survival and censored data.Bayesian Weibull tree models for survival analysis of clinico-genomic dataPredicting survival outcomes using subsets of significant genes in prognostic marker studies with microarraysIdentification of genes that regulate multiple cellular processes/responses in the context of lipotoxicity to hepatoma cells.A predictive risk probability approach for microarray data with survival as an endpointDimension reduction of microarray data in the presence of a censored survival response: a simulation study.Dimension reduction of microarray gene expression data: the accelerated failure time model.Pathway analysis using random forests with bivariate node-split for survival outcomesBayesian ensemble methods for survival prediction in gene expression dataUsing cross-validation to evaluate predictive accuracy of survival risk classifiers based on high-dimensional data.Sequential interim analyses of survival data in DNA microarray experimentsA hybrid approach of gene sets and single genes for the prediction of survival risks with gene expression data.Bayesian profiling of molecular signatures to predict event times.Survival analysis with high-dimensional covariates.MiRKAT-S: a community-level test of association between the microbiota and survival times.Standardized genetic alteration score and predicted score for predicting recurrence status of gastric cancer.Analysis of additive risk model with high-dimensional covariates using partial least squares.The additive hazards model with high-dimensional regressors.Supervised wavelet method to predict patient survival from gene expression data.Bayesian Weibull Survival Model for Gene Expression DataClassification based on extensions of LS-PLS using logistic regression: application to clinical and multiple genomic dataHigh-Dimensional Cox Regression Analysis in Genetic Studies with Censored Survival Outcomes
P2860
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P2860
Linking gene expression data with patient survival times using partial least squares.
description
2002 nî lūn-bûn
@nan
2002 թուականի Յունուարին հրատարակուած գիտական յօդուած
@hyw
2002 թվականի հունվարին հրատարակված գիտական հոդված
@hy
2002年の論文
@ja
2002年論文
@yue
2002年論文
@zh-hant
2002年論文
@zh-hk
2002年論文
@zh-mo
2002年論文
@zh-tw
2002年论文
@wuu
name
Linking gene expression data with patient survival times using partial least squares.
@ast
Linking gene expression data with patient survival times using partial least squares.
@en
type
label
Linking gene expression data with patient survival times using partial least squares.
@ast
Linking gene expression data with patient survival times using partial least squares.
@en
prefLabel
Linking gene expression data with patient survival times using partial least squares.
@ast
Linking gene expression data with patient survival times using partial least squares.
@en
P356
P1433
P1476
Linking gene expression data with patient survival times using partial least squares.
@en
P2093
P304
P356
10.1093/BIOINFORMATICS/18.SUPPL_1.S120
P407
P478
18 Suppl 1
P50
P577
2002-01-01T00:00:00Z