A General Framework for Identifying Hierarchical Interactions and Its Application to Genomics Data.

ABSTRACT: The analysis of hierarchical interactions has long been a challenging problem due to the large number of candidate main effects and interaction effects, and the need for accommodating the "main effects, interactions" hierarchy. The two-stage analysis methods enjoy simplicity and low computational cost, but contradict the fact that the outcome of interest is attributable to the joint effects of multiple main factors and their interactions. The existing joint analysis methods can accurately describe the underlying data generating process, but suffer from prohibitively high computational cost. And it is not straightforward to extend their optimization algorithms to general loss functions. To address this need, we develop a new computational method that is much faster than the existing joint a

SUBMITTER: Xiao Z 

PROVIDER: S-EPMC10671243 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature