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

You Wu, Lei Xie

TL;DR

The paper addresses the challenge of predicting phenotypes from genotypes under environmental perturbations by advocating a biology-inspired, AI-driven multi-scale framework that integrates multi-omics across cellular to organismal levels and across species. It surveys perturbation-omics resources and state-of-the-art ML methods (unsupervised, supervised, transformers, and knowledge-graph approaches), identifies key limitations such as data scarcity, distribution shifts, and noisy graphs, and proposes two complementary AI-powered strategies: end-to-end multi-modal deep learning and physics-informed, context-specific knowledge graphs, with integration of generative AI. The authors argue that endophenotypes offer a tractable bridge between genotype and phenotype, and that a hybrid approach combining ML, mechanistic modeling, and graphs can uncover causal G-E-P relationships, enabling discovery of targets, biomarkers, and personalized therapies. The work emphasizes the potential impact on precision medicine and cross-species translation, while acknowledging the need for digital twins to realize robust, interpretable predictions in real-world settings.

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 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.

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

TL;DR

The paper addresses the challenge of predicting phenotypes from genotypes under environmental perturbations by advocating a biology-inspired, AI-driven multi-scale framework that integrates multi-omics across cellular to organismal levels and across species. It surveys perturbation-omics resources and state-of-the-art ML methods (unsupervised, supervised, transformers, and knowledge-graph approaches), identifies key limitations such as data scarcity, distribution shifts, and noisy graphs, and proposes two complementary AI-powered strategies: end-to-end multi-modal deep learning and physics-informed, context-specific knowledge graphs, with integration of generative AI. The authors argue that endophenotypes offer a tractable bridge between genotype and phenotype, and that a hybrid approach combining ML, mechanistic modeling, and graphs can uncover causal G-E-P relationships, enabling discovery of targets, biomarkers, and personalized therapies. The work emphasizes the potential impact on precision medicine and cross-species translation, while acknowledging the need for digital twins to realize robust, interpretable predictions in real-world settings.

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 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.
Paper Structure (20 sections, 4 figures)

This paper contains 20 sections, 4 figures.

Table of Contents

  1. Introduction
  2. Perturbation omics data resources
  3. State-of-the-art of machine learning methods for multi-omics data integration and predictive modeling
  4. Unsupervised learning
  5. Autoencoder
  6. Transformer
  7. Other techniques (contrastive learning etc.)
  8. Supervised learning
  9. Multi-modal supervised learning
  10. Knowledge graph and other techniques
  11. Challenges in machine learning techniques
  12. Need for biologically informed representation learning
  13. Scarcity and ambiguity of labeled data
  14. Inability to generalize out-of-distribution
  15. Incomplete and noisy graphs
  16. ...and 5 more sections

Figures (4)

  • Figure 1: Illustration of cross-level, cross-scale, cross-species multi-omics data integration
  • Figure 2: Illustration of multi-modal supervised learning. (a) A conventional strategy that requires paired data for all the modalities simultaneously. (b) An end-to-end deep neural network explicitly models asymmetric information flows from DNAs to RNAs to proteins to metabolites and ultimately to the organismal phenotype. Each modality can be pre-trained using unlabeled data. The paired data is used to fine-tune the model between any two modalities. After the model is fully trained, phenotypes can be predicted from genotypes through endophenotypes even if their data is not available.
  • Figure 3: Illustration of personalized physics-informed multi-scale knowledge graph. It represents causal genotype-environment-phenotype from a single cell to an individual.
  • Figure 4: Integration of machine learning models, mechanistic models, knowledge graphs, and generative AI