Boosting for high-dimensional time-to-event data with competing risks.
about
Selective genomic copy number imbalances and probability of recurrence in early-stage breast cancerCombining techniques for screening and evaluating interaction terms on high-dimensional time-to-event data.Gene selection for survival data under dependent censoring: A copula-based approach.High-dimensional additive hazards regression for oral squamous cell carcinoma using microarray data: a comparative studyCompeting risks data analysis with high-dimensional covariates: an application in bladder cancer.Predicting the Survival Time for Bladder Cancer Using an Additive Hazards Model in Microarray Data.Random rotation survival forest for high dimensional censored dataLearning rule sets from survival data.An overview of techniques for linking high-dimensional molecular data to time-to-event endpoints by risk prediction models.Random survival forests for competing risks.Increased microglia/macrophage gene expression in a subset of adult and pediatric astrocytomasGene expression profiling predicts the development of oral cancer.Absolute risk regression for competing risks: interpretation, link functions, and predictionDealing with prognostic signature instability: a strategy illustrated for cardiovascular events in patients with end-stage renal diseasePrediction accuracy and variable selection for penalized cause-specific hazards models.A random forest approach for competing risks based on pseudo-values.Machine learning for prediction of all-cause mortality in patients with suspected coronary artery disease: a 5-year multicentre prospective registry analysis.Quantifying the predictive accuracy of time-to-event models in the presence of competing risks.Penalized estimation for competing risks regression with applications to high-dimensional covariates.Stagewise pseudo-value regression for time-varying effects on the cumulative incidence.A joint frailty-copula model between tumour progression and death for meta-analysis.Tailoring sparse multivariable regression techniques for prognostic single-nucleotide polymorphism signatures.Coupled variable selection for regression modeling of complex treatment patterns in a clinical cancer registry.Baseline splenic volume as a surrogate marker of FOLFIRINOX efficacy in advanced pancreatic carcinoma.
P2860
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P2860
Boosting for high-dimensional time-to-event data with competing risks.
description
2009 nî lūn-bûn
@nan
2009 թուականի Փետրուարին հրատարակուած գիտական յօդուած
@hyw
2009 թվականի փետրվարին հրատարակված գիտական հոդված
@hy
2009年の論文
@ja
2009年論文
@yue
2009年論文
@zh-hant
2009年論文
@zh-hk
2009年論文
@zh-mo
2009年論文
@zh-tw
2009年论文
@wuu
name
Boosting for high-dimensional time-to-event data with competing risks.
@ast
Boosting for high-dimensional time-to-event data with competing risks.
@en
type
label
Boosting for high-dimensional time-to-event data with competing risks.
@ast
Boosting for high-dimensional time-to-event data with competing risks.
@en
prefLabel
Boosting for high-dimensional time-to-event data with competing risks.
@ast
Boosting for high-dimensional time-to-event data with competing risks.
@en
P2093
P356
P1433
P1476
Boosting for high-dimensional time-to-event data with competing risks.
@en
P2093
Arthur Allignol
Jan Beyersmann
Martin Schumacher
P304
P356
10.1093/BIOINFORMATICS/BTP088
P407
P577
2009-02-25T00:00:00Z