MetabolicSurv

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) doi:10.1016/0169-7439(87)80084-9, the LASSO method by Robert Tibshirani (1998) doi:10.1002/(SICI)1097-0258(19970228)16:4<385::AID-SIM380>3.0.CO;2-31097-0258(19970228)16:4%3C385::AID-SIM380%3E3.0.CO;2-3), the elastic net approach by Hui Zou and Trevor Hastie (2005) doi:10.1111/j.1467-9868.2005.00503.x. 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.

Last 30 days

This package has been downloaded 181 times in the last 30 days. More than a random curiosity, but not quite a blockbuster. Still, it's gaining traction! The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 2 times.

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Last 365 days

This package has been downloaded 2,144 times in the last 365 days. Consider this 'mid-tier influencer' status—if it were a TikTok, it would get a nod from nieces and nephews. The day with the most downloads was Jul 24, 2024 with 25 downloads.

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