BioMod: Biological Module Tokenization for Single-Cell Foundation Modeling

Single-cell foundation models typically represent cells as flat sequences of individual genes, requiring higher-order biological organization to be inferred implicitly from sparse expression profiles. Similar to the evolution from character-level to semantic tokenization in natural language processing,we hypothesize that cellular states can be more efficiently represented using biologically meaningful intermediate units. Here we present BioMod, a 50-million-parameter single-cell foundation model that introduces biological modules as representation units between genes and cells. BioMod constructs an overlapping vocabulary of 660 biological modules derived from curated pathways and data-driven gene programs, replacing thousands of gene-level inputs with compact module representations. Pretrained on approximately 73 million human cells using high-ratio module masking with an asymmetric decoder, BioMod achieves state-of-the-art performance across cell-type annotation, zero-shot batch integration, perturbation-response prediction and drug-response forecasting while maintaining a compact architecture that enables efficient inference on a single GPU. Module-level analyses further demonstrate that BioMod captures biologically meaningful cellular programs and preserves pathway-level organization.