AI-driven multi-omics integration for multi-scale predictive modeling of causal genotype-environment-phenotype relationships
This addresses the problem of limited predictive power in biomedicine for researchers and clinicians, though it appears incremental as it builds on existing multi-omics integration methods.
The paper tackles the challenge of predicting the effects of genetic and chemical perturbations in humans by integrating multi-omics data across biological scales, aiming to identify causal relationships rather than correlations, with potential applications in discovering new molecular targets and personalized medicines.
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 modeling framework to tackle these issues. This framework will integrate multi-omics data across biological levels, organism hierarchies, and species to predict causal genotype-environment-phenotype relationships under various conditions. AI models inspired by biology may identify novel molecular targets, biomarkers, pharmaceutical agents, and personalized medicines for presently unmet medical needs.