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Multimodal Learning for Mapping the Genotype-Phenotype Dynamics.

Farhan Khodaee1, Rohola Zandie1, Elazer R Edelman1,2

  • 1Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, 02139, MA, USA.

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Summary
This summary is machine-generated.

This study introduces an integrated genetics framework using AI to analyze gene expression and cell traits. The approach reveals deeper insights into cellular diversity, gene interactions, and aging, advancing our understanding of complex biological systems.

Keywords:
integrated geneticslanguage modelingmultimodal foundation modelpolyfunctional genesingle-cell RNA sequencingtransformer

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Area of Science:

  • Genomics and Computational Biology
  • Cellular and Molecular Biology

Background:

  • Understanding complex phenotypes from gene expression is a key biological challenge.
  • Current methods struggle with the vast combinatorial possibilities and dynamic genotype-phenotype interplay.

Purpose of the Study:

  • To develop a computational framework for analyzing high-dimensional genotype and phenotype data simultaneously.
  • To create a multimodal foundation model for exploring the human transcriptomic genotype-phenotype relationship at the cellular level.

Main Methods:

  • Integrated genetics framework combining high-content genotyping (single-cell RNA sequencing) and advanced learning (language models).
  • Development of a multimodal foundation model to analyze the joint genotype-phenotype manifold.
  • Utilized contextualized embeddings to investigate gene polyfunctionality.

Main Results:

  • Refined resolution of cellular heterogeneity and enhanced phenotype annotation.
  • Uncovered cross-tissue biomarkers missed by conventional analysis.
  • Identified scale-free gene networks with context-dependent interactions, showing variations during aging.
  • Demonstrated gene polyfunctionality using contextualized embeddings, exemplified by the VWF gene in endothelial cells.

Conclusions:

  • The integrated genetics framework advances understanding of gene expression and phenotypic manifestation.
  • Highlights the potential of integrated genetics in uncovering new dimensions of cellular function and complexity.
  • Reveals dynamic and context-dependent gene-gene interactions, particularly significant in aging processes.