AI-driven multi-omics integration for multi-scale predictive modeling of genotype-environment-phenotype relationships.

ABSTRACT: Despite the wealth of single-cell multi-omics data, it remains challenging to predict the consequences of novel genetic and chemical perturbations in the human body. It requires knowledge of molecular interactions at all biological levels, encompassing disease models and humans. Current machine learning methods primarily establish statistical correlations between genotypes and phenotypes but struggle to identify physiologically significant causal factors, limiting their predictive power. Key challenges in predictive modeling include scarcity of labeled data, generalization across different domains, and disentangling causation from correlation. In light of recent advances in multi-omics data integration, we propose a new artificial intelligence (AI)-powered biology-inspired multi-scale mode

SUBMITTER: Wu Y 

PROVIDER: S-EPMC11779603 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature