A unified framework enables accessible deployment and comprehensive benchmarking of single-cell foundation models.

ABSTRACT: Recent years have seen rapid growth in single-cell foundation models (scFMs), raising expectations for transformative advances in genomic data analysis. However, their adoption has been hindered by inconsistent performance across datasets, fragmented software ecosystems, high technical barriers, and the lack of best practices established through systematic, reproducible benchmarks. Here we present a unified, extensible, and fully automated computational framework that standardizes the execution, evaluation, and extension of diverse scFMs. The framework harmonizes software environments, eliminates manual configuration, and enables large-scale, reproducible evaluation across heterogeneous datasets and training regimes. Leveraging this infrastructure, we systematically benchmark thirteen foun

SUBMITTER: Hou S 

PROVIDER: S-EPMC12803055 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature