Towards multimodal foundation models in molecular cell biology

Area of Science:

• Computational Biology
• Genomics
• Molecular Biology

Background:

• High-throughput omics technologies generate vast biological data, challenging traditional analysis methods.
• Large-language models (LLMs) have successfully integrated massive datasets in natural language processing.
• A need exists to bridge the gap between omics data generation and molecular insight discovery.

Purpose of the Study:

• To propose the development of multimodal foundation models pretrained on diverse omics datasets.
• To leverage these models for characterizing cellular molecular states and creating comprehensive biological maps.
• To enable advanced AI-driven applications in molecular cell biology and life sciences.

Main Methods:

• Pretraining multimodal foundation models on integrated omics data (genomics, transcriptomics, epigenomics, proteomics, metabolomics, spatial profiling).
• Utilizing context-specific transfer learning for downstream applications.
• Developing AI-powered analytical frameworks for biological data.

Main Results:

• Anticipated ability to characterize molecular cell states across a continuum.
• Facilitation of holistic mapping of cells, genes, and tissues.
• Potential for novel cell-type recognition, biomarker discovery, and gene regulation inference.

Conclusions:

• Multimodal foundation models represent a paradigm shift in analyzing complex biological data.
• AI-empowered analyses can unravel intricate molecular mechanisms and support experimental design.
• This approach promises to significantly advance our understanding of life sciences.