Sparse Regression in Cancer Genomics: Comparing Variable Selection and Predictions in Real World Data.

ABSTRACT:

Background

Evaluation of gene interaction models in cancer genomics is challenging, as the true distribution is uncertain. Previous analyses have benchmarked models using synthetic data or databases of experimentally verified interactions - approaches which are susceptible to misrepresentation and incompleteness, respectively. The objectives of this analysis are to (1) provide a real-world data-driven approach for comparing performance of genomic model inference algorithms, (2) compare the performance of LASSO, elastic net, best-subset selection, L0L1 penalisation and L0L2 penalisation in real genomic data and (3) compare algorithmic preselection according to performance in our benchmark datasets to algorithmic selection by internal cross-validation.

Methods

Five large (n400

SUBMITTER: O'Shea RJ 

PROVIDER: S-EPMC8640984 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature