Foundation models (FMs) have recently transformed single-cell genomics by learning transferable representations from large-scale single-cell data, enabling a wide range of downstream biomedical applications. Inspired by natural language processing, existing single-cell FMs adapt transformer architectures by treating ge...
Kaichen Xu, Mianpeng Liu, Hao Wu et al.· Proceedings of the 32nd ACM...· 0 citations
Cell type standardization plays a central role in integrating biological knowledge across single-cell studies. While standardized resources (e.g., Cell Ontology, Nomenclature Frameworks) provide unified vocabularies of cell populations, scientific publications and public datasets continue to use heterogeneous study-spe...
Peng Xie, Rongjia Zhou, Zhi-Li Ou et al.· arXiv.org· 0 citations
Predicting drug synergy is essential for optimizing combination therapies in cancer treatment. Under extreme data scarcity, existing computational methods struggle to generalize to new cell lines. Although meta-learning approaches have shown promise, a critical limitation lies in their reliance on a unimodal Gaussian p...
Shuting Jin, Xu Guo, Anqi Huang et al.· Proceedings of the 32nd ACM...· 0 citations
Foundation models (FMs) have recently transformed single-cell genomics by learning transferable representations from large-scale single-cell data, enabling a wide range of downstream biomedical applications. Inspired by natural language processing, existing single-cell FMs adapt transformer architectures by treating ge...
Kaichen Xu, Mianpeng Liu, Hao Wu et al.· Proceedings of the 32nd ACM...· 0 citations
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