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

arXiv: https://arxiv.org/abs/2407.06405 · HTML: https://mlsanity.com/paper/2407.06405/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 123 references that are in the map.
- First posted on arXiv 8 Jul 2024.

## Builds on (most cited first, 15 of 15)

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- [BioBridge: Bridging Biomedical Foundation Models via Knowledge Graphs](https://mlsanity.com/paper/2310.03320/) (2023)
- [Human Body Digital Twin: A Master Plan](https://mlsanity.com/paper/2307.09225/) (2023)
- [PIMKL: Pathway Induced Multiple Kernel Learning](https://mlsanity.com/paper/1803.11274/) (2018)

## Abstract (arXiv)

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.