Guiding adaptive shrinkage by co-data to improve regression-based prediction and feature selection

arXiv:2405.04917v1 Announce Type: cross
Abstract: The high dimensional nature of genomics data complicates feature selection, in particular in low sample size studies - not uncommon in clinical prediction settings. It is widely recognized that complementary data on the features, `co-data', may improve results. Examples are prior feature groups or p-values from a related study. Such co-data are ubiquitous in genomics settings due to the availability of public repositories. Yet, the uptake of learning methods that structurally use such co-data is …

abstract arxiv clinical data examples feature features feature selection genomics low nature prediction prior regression results sample shrinkage stat.me stat.ml studies type