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Citations per year

Number of citations per year for the bioinformatics software tool CoxBoost

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CoxBoost specifications


Unique identifier OMICS_14238
Name CoxBoost
Software type Package/Module
Interface Command line interface
Restrictions to use None
Operating system Unix/Linux, Mac OS, Windows
Programming languages R
License GNU General Public License version 3.0, GNU General Public License version 2.0
Computer skills Advanced
Version 1.4
Stability Stable
Survival, Matrix, Prodlim
Source code URL
Maintained Yes


No version available



  • person_outline Veronika Weyer

Publications for CoxBoost

CoxBoost citations


Prediction of Early Breast Cancer Metastasis from DNA Microarray Data Using High Dimensional Cox Regression Models

Cancer Inform
PMCID: 4426954
PMID: 25983547
DOI: 10.4137/CIN.S17284

[…] e 10,002 genes common to the van’t Veer’s, van de Vijver’s, and Desmedt’s datasets. The five-fold cross-validation procedure identified 6, 18, and 29 as the optimal number of predictive genes for the CoxBoost, LASSO, and Elastic net methods, respectively.The three high-dimensional Cox regression models with automatic selection of the tuning parameter by cross-validation led to the definition of pr […]


Competing Risks Data Analysis with High dimensional Covariates: An Application in Bladder Cancer

PMCID: 4563215
PMID: 25907251
DOI: 10.1016/j.gpb.2015.04.001

[…] tly-included variables or FP, and the proportion of correctly-excluded variables or TN were calculated.In this study, all analyses were implemented using the R software packages including “fastcox”, “CoxBoost”, “cmprsk”, “survAUC”, and “pec” ( […]


Combining techniques for screening and evaluating interaction terms on high dimensional time to event data

BMC Bioinformatics
PMCID: 3945780
PMID: 24571520
DOI: 10.1186/1471-2105-15-58

[…] blocks are important. Our decision for using random forests for screening interactions has one main reason: the promise of random forests to capture various kinds of relevant interaction structures. CoxBoost was used, because it usually produces sparse risk prediction models. We assumed that a combination of these two approaches could be fruitful due to their complementary character. However, com […]

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CoxBoost institution(s)
Institute of Medical Biostatistics, Epidemiology and Informatics (IMBEI), University Medical Center Mainz, Johannes Gutenberg-University Mainz, Mainz, Germany
CoxBoost funding source(s)
This work was supported by the German Research Foundation DFG BI 1433/2-1.

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