Charlie Kim

Single-cell foundation models benefit from cross-modal training: adding proteomics data beats parameter scaling

It is shown that with the right training recipe, heterogeneous proteomics data can improve the learned representations of single-cell RNAseq samples, demonstrating strong out-of-distribution generalization and suggesting that multimodal pretraining is a promising path toward more informative biological foundation model...