An overview of techniques for linking high-dimensional molecular data to time-to-event endpoints by risk prediction models.
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Big data in medical science--a biostatistical viewTransforming RNA-Seq data to improve the performance of prognostic gene signatures.Assessment of performance of survival prediction models for cancer prognosisA regression-based differential expression detection algorithm for microarray studies with ultra-low sample sizeIntegrated genomic analysis identifies subclasses and prognosis signatures of kidney cancer.Identifying Prognostic SNPs in Clinical Cohorts: Complementing Univariate Analyses by Resampling and Multivariable Modeling.Coupled variable selection for regression modeling of complex treatment patterns in a clinical cancer registry.Probability estimation and machine learning--Editorial.
P2860
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P2860
An overview of techniques for linking high-dimensional molecular data to time-to-event endpoints by risk prediction models.
description
2011 nî lūn-bûn
@nan
2011 թուականի Փետրուարին հրատարակուած գիտական յօդուած
@hyw
2011 թվականի փետրվարին հրատարակված գիտական հոդված
@hy
2011年の論文
@ja
2011年論文
@yue
2011年論文
@zh-hant
2011年論文
@zh-hk
2011年論文
@zh-mo
2011年論文
@zh-tw
2011年论文
@wuu
name
An overview of techniques for ...... nts by risk prediction models.
@ast
An overview of techniques for ...... nts by risk prediction models.
@en
type
label
An overview of techniques for ...... nts by risk prediction models.
@ast
An overview of techniques for ...... nts by risk prediction models.
@en
prefLabel
An overview of techniques for ...... nts by risk prediction models.
@ast
An overview of techniques for ...... nts by risk prediction models.
@en
P2093
P2860
P356
P1433
P1476
An overview of techniques for ...... nts by risk prediction models.
@en
P2093
Christine Porzelius
Harald Binder
Martin Schumacher
P2860
P304
P356
10.1002/BIMJ.201000152
P577
2011-02-17T00:00:00Z