Towards Multimodal Foundation Models in Molecular Cell Biology

Abstract

The rapid advent of high-throughput omics technologies has created an exponential growth in biological data, often outpacing our ability to derive molecular insights. Large-language models have shown a way out of this data deluge in natural language processing by integrating massive data sets into a joint model with manifold downstream use cases. Here, we envision developing multimodal foundation models, pretrained on diverse omics datasets, including genomics, transcriptomics, epigenomics, proteomics, metabolomics, and spatial profiling. These models are expected to exhibit unprecedented potential for characterizing the molecular states of cells across a broad continuum, thereby facilitating the creation of holistic maps of cells, genes, and tissues. Context-specific transfer learning of the foundation models can empower diverse applications from novel cell type recognition, biomarker discovery, gene regulation inference, to in silico perturbations. This new paradigm could launch an era of AI-empowered analyses, one that promises to unravel the intricate complexities of molecular cell biology, to support experimental design, and, more broadly, to profoundly extend our understanding of life sciences.