Extensions of Heterogeneity in Integration and Prediction (HIP) With R Shiny Application.

ABSTRACT: Multiple data views measured on the same set of participants are becoming more common and have the potential to deepen our understanding of many complex diseases by analyzing these different views simultaneously. Equally important, many of these complex diseases show evidence of subgroup heterogeneity (e.g., by sex or race). HIP (Heterogeneity in Integration and Prediction) is among the first methods proposed to integrate multiple data views while also accounting for subgroup heterogeneity to identify common and subgroup-specific markers of a particular disease. However, HIP is applicable to continuous outcomes and requires programming expertise by the user. Here we propose extensions to HIP that accommodate multi-class, Poisson, and Zero-Inflated Poisson outcomes while retaining the benef

SUBMITTER: Butts J 

PROVIDER: S-EPMC12023842 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature