Partial Cox regression analysis for high-dimensional microarray gene expression data.
about
Epidermal growth factor receptor tyrosine kinase defines critical prognostic genes of stage I lung adenocarcinomaA gene expression signature predicts survival of patients with stage I non-small cell lung cancerBoosting the concordance index for survival data--a unified framework to derive and evaluate biomarker combinations.Deviance residuals-based sparse PLS and sparse kernel PLS regression for censored data.Integration of pathway structure information into a reweighted partial Cox regression approach for survival analysis on high-dimensional gene expression data.Boosting proportional hazards models using smoothing splines, with applications to high-dimensional microarray data.Penalized Cox regression analysis in the high-dimensional and low-sample size settings, with applications to microarray gene expression data.Additive risk models for survival data with high-dimensional covariates.Predicting patient survival from microarray data by accelerated failure time modeling using partial least squares and LASSO.Doubly penalized buckley-james method for survival data with high-dimensional covariates.SignS: a parallelized, open-source, freely available, web-based tool for gene selection and molecular signatures for survival and censored data.Sparse kernel methods for high-dimensional survival data.Predicting survival outcomes using subsets of significant genes in prognostic marker studies with microarraysImprove survival prediction using principal components of gene expression dataAssessment of survival prediction models based on microarray data.Dimension reduction of microarray data in the presence of a censored survival response: a simulation study.Iterative Bayesian Model Averaging: a method for the application of survival analysis to high-dimensional microarray data.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.Gene-expression signature predicts postoperative recurrence in stage I non-small cell lung cancer patients.Gene expression profiling for survival prediction in pediatric rhabdomyosarcomas: a report from the children's oncology group.Survival analysis tools in genomics researchNetwork-based survival analysis reveals subnetwork signatures for predicting outcomes of ovarian cancer treatment.ROC-supervised principal component analysis in connection with the diagnosis of diseasesInvestigating the utility of clinical outcome-guided mutual information network in network-based Cox regression.Extending information retrieval methods to personalized genomic-based studies of disease.Bayesian profiling of molecular signatures to predict event times.Fast dimension reduction and integrative clustering of multi-omics data using low-rank approximation: application to cancer molecular classification.MicroRNA profiling and prediction of recurrence/relapse-free survival in stage I lung cancer.CRC-113 gene expression signature for predicting prognosis in patients with colorectal cancer.L1 penalized continuation ratio models for ordinal response prediction using high-dimensional datasetsClinical and microarray analysis of breast cancers of all subtypes from two prospective preoperative chemotherapy studies.The Dantzig Selector for Censored Linear Regression Models.Survival analysis with high-dimensional covariates.Systematic identification of transcription factors associated with patient survival in cancers.Identification of early-stage lung adenocarcinoma prognostic signatures based on statistical modeling.OSAnalyzer: A Bioinformatics Tool for the Analysis of Gene Polymorphisms Enriched with Clinical OutcomesJoint Covariate Detection on Expression Profiles for Selecting Prognostic miRNAs in Glioblastoma.
P2860
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P2860
Partial Cox regression analysis for high-dimensional microarray gene expression data.
description
2004 nî lūn-bûn
@nan
2004 թուականի Օգոստոսին հրատարակուած գիտական յօդուած
@hyw
2004 թվականի օգոստոսին հրատարակված գիտական հոդված
@hy
2004年の論文
@ja
2004年論文
@yue
2004年論文
@zh-hant
2004年論文
@zh-hk
2004年論文
@zh-mo
2004年論文
@zh-tw
2004年论文
@wuu
name
Partial Cox regression analysis for high-dimensional microarray gene expression data.
@ast
Partial Cox regression analysis for high-dimensional microarray gene expression data.
@en
type
label
Partial Cox regression analysis for high-dimensional microarray gene expression data.
@ast
Partial Cox regression analysis for high-dimensional microarray gene expression data.
@en
prefLabel
Partial Cox regression analysis for high-dimensional microarray gene expression data.
@ast
Partial Cox regression analysis for high-dimensional microarray gene expression data.
@en
P356
P1433
P1476
Partial Cox regression analysis for high-dimensional microarray gene expression data.
@en
P2093
P304
P356
10.1093/BIOINFORMATICS/BTH900
P407
P478
20 Suppl 1
P577
2004-08-01T00:00:00Z