Foundation Models Enable Cross-Modal Translation Between Spatial and Single-Cell RNA Sequencing Data
Two new research papers demonstrate applications of foundation models: one shows adversarial fine-tuning can translate between spatial transcriptomics and single-cell RNA sequencing data without paired datasets, while the other evaluates seven fine-tuning strategies for machine-learned interatomic potentials across diverse chemical systems. These studies address practical challenges in adapting pre-trained foundation models to specialized scientific tasks. The findings are significant because they provide systematic guidance for researchers seeking to leverage foundation models in biology and computational chemistry, where obtaining large paired datasets or training models from scratch is often prohibitively expensive.
Two independent preprints from arXiv present complementary advances in foundation model adaptation. The first paper proposes using adversarial fine-tuning to perform cross-modal translation between spatial transcriptomics (ST) and single-cell RNA sequencing (scRNA-seq) data, addressing the scarcity of paired datasets by leveraging the abundance of each modality separately. The authors demonstrate that their approach outperforms existing multi-omics translation methods. The second paper systematically evaluates seven fine-tuning strategies for machine-learned interatomic potential (MLIP) foundation models across five chemically diverse benchmarks and three model generations, with training sets spanning five orders of magnitude. Key findings include that foundation model quality, correct initialization, and hyperparameter selection matter more than the fine-tuning strategy itself, though multihead replay with pseudolabelled data uniquely preserves out-of-distribution robustness. Together, these studies provide practical guidance for adapting foundation models in specialized scientific domains.
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- arXiv physicsCenter
Fine-tuning MLIP foundation models: strategies for accuracy and transferability
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