| Name : R-MetabolicSurv
| |
| Version : 1.1.2
| Vendor : obs://build_opensuse_org/devel:languages:R
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| Release : lp155.29.1
| Date : 2024-06-27 23:10:33
|
| Group : Development/Libraries/Other
| Source RPM : R-MetabolicSurv-1.1.2-lp155.29.1.src.rpm
|
| Size : 0.56 MB
| |
| Packager : https://www_suse_com/
| |
| Summary : A Biomarker Validation Approach for Classification and Predicting Survival Using Metabolomics Signature
|
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)
< doi:10.1016/0169-7439(87)80084-9> , the LASSO method by Robert
Tibshirani (1998)
< doi:10.1002/(SICI)1097-0258(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.
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RPM found in directory: /packages/linux-pbone/ftp5.gwdg.de/pub/opensuse/repositories/devel:/languages:/R:/autoCRAN/15.5/x86_64 |
