CoxBoost statistics

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Citations per year

Number of citations per year for the bioinformatics software tool CoxBoost
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CoxBoost specifications

Information


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
Requirements
Survival, Matrix, Prodlim
Source code URL https://cran.r-project.org/src/contrib/CoxBoost_1.4.tar.gz
Maintained Yes

Versioning


No version available

Documentation


Maintainer


  • person_outline Veronika Weyer

Publications for CoxBoost

CoxBoost citations

 (3)
library_books

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

2015
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 […]

library_books

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

2015
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” (http://www.r-project.org). […]

library_books

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

2014
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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