get_opt_models: Get the cancer-specific model feature parameters

In olapuentesantana/easierData: easier internal data and exemplary dataset from IMvigor210CoreBiologies package

Description

Obtain the cancer-specific model feature parameters learned in Lapuente-Santana et al. (2021). For each quantitative descriptor, models were trained using multi-task learning with randomized cross-validation repeated 100 times. For each quantitative descriptor, 1000 models are available (100 per task).

Usage

get_opt_models()

Value

A list containing for each cancer type and quantitative descriptor, a matrix of feature coefficient values across different tasks. The cancer types, for which a cancer-specific model is available, are: "BLCA", "BRCA", "CESC", "CRC", "GBM", "HNSC", "KIRC", "KIRP", "LIHC", "LUAD", "LUSC", "NSCLC", "OV", "PAAD", "PRAD", "SKCM", "STAD", "THCA" and "UCEC".

References

Óscar Lapuente-Santana, Maisa van Genderen, Peter A. J. Hilbers, Francesca Finotello, and Federica Eduati. 2021. Interpretable Systems Biomarkers Predict Response to Immune-Checkpoint Inhibitors. Patterns, 100293. https://doi.org/10.1016/j.patter.2021.100293.

Examples

if (interactive()) {
    opt_models <- get_opt_models()
}

olapuentesantana/easierData documentation built on Dec. 22, 2021, 4:19 a.m.