GE-IA-NAM: gene-environment interaction analysis via imaging-assisted neural additive model.

ABSTRACT:

Motivation

Gene-environment (G-E) interaction analysis is crucial in cancer research, offering insights into how genetic and environmental factors jointly influence cancer outcomes. Most existing G-E interaction methods are regression-based, which may lack flexibility to capture complex data patterns. Recent advances have investigated deep neural network-based G-E models. However, these methods may be more vulnerable to information deficiency due to challenges such as limited sample size and high dimensionality. Apart from genetic and environmental data, pathological images have emerged as a widely accessible and informative resource for cancer modeling, presenting its potential to enhance G-E modeling.

Results

We propose the pathological imaging-assisted neural additive mod

SUBMITTER: Li J 

PROVIDER: S-EPMC12452269 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature