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MetabolicSurv  

A Biomarker Validation Approach for Classification and Predicting Survival Using Metabolomics Signature
View on CRAN: Click here


Download and install MetabolicSurv package within the R console
Install from CRAN:
install.packages("MetabolicSurv")

Install from Github:
library("remotes")
install_github("cran/MetabolicSurv")

Install by package version:
library("remotes")
install_version("MetabolicSurv", "1.1.2")



Attach the package and use:
library("MetabolicSurv")
Maintained by
Olajumoke Evangelina Owokotomo
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2019-01-31
Latest Update: 2021-06-11
Description:
An approach to identifies metabolic biomarker signature for metabolic data by discovering predictive metabolite for predicting survival and classifying patients into risk groups. Classifiers are constructed as a linear combination of predictive/important metabolites, prognostic factors and treatment effects if necessary. Several methods were implemented to reduce the metabolomics matrix such as the principle component analysis of Wold Svante et al. (1987) , the LASSO method by Robert Tibshirani (1998) , the elastic net approach by Hui Zou and Trevor Hastie (2005) . Sensitivity analysis on the quantile used for the classification can also be accessed to check the deviation of the classification group based on the quantile specified. Large scale cross validation can be performed in order to investigate the mostly selected predictive metabolites and for internal validation. During the evaluation process, validation is accessed using the hazard ratios (HR) distribution of the test set and inference is mainly based on resampling and permutations technique.
How to cite:
Olajumoke Evangelina Owokotomo (2019). MetabolicSurv: A Biomarker Validation Approach for Classification and Predicting Survival Using Metabolomics Signature. R package version 1.1.2, https://cran.r-project.org/web/packages/MetabolicSurv. Accessed 22 Dec. 2024.
Previous versions and publish date:
1.0.0 (2019-01-31 00:16), 1.1.0 (2019-08-05 12:20), 1.1.1 (2020-08-24 15:20)
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