Computationally scalable regression modeling for ultrahigh-dimensional omics data with ParProx.

ABSTRACT: Statistical analysis of ultrahigh-dimensional omics scale data has long depended on univariate hypothesis testing. With growing data features and samples, the obvious next step is to establish multivariable association analysis as a routine method to describe genotype-phenotype association. Here we present ParProx, a state-of-the-art implementation to optimize overlapping and non-overlapping group lasso regression models for time-to-event and classification analysis, with selection of variables grouped by biological priors. ParProx enables multivariable model fitting for ultrahigh-dimensional data within an architecture for parallel or distributed computing via latent variable group representation. It thereby aims to produce interpretable regression models consistent with known biological

SUBMITTER: Ko S 

PROVIDER: S-EPMC8575036 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature