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Proceedings of the AAAI Conference on Artificial Intelligence

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PanFoMa: A Lightweight Foundation Model and Benchmark for...
Xiaoshui Hua · 2026-03-14 · via Proceedings of the AAAI Conference on Artificial Intelligence

Authors

  • Xiaoshui Huang Shanghai Jiaotong University
  • Tianlin Zhu Jiangxi University of Finance and Economics
  • Yifan Zuo Jiangxi University of Finance and Economics
  • Xue Xia Jiangxi University of Finance and Economics
  • Zonghan Wu East China Normal University
  • Jiebin Yan Jiangxi University of Finance and Economics
  • Dingli Hua Jiangxi University of Finance and Economics
  • Zongyi Xu Chongqing University of Post and Telecommunications
  • Yuming Fang Jiangxi University of Finance and Economics
  • Jian Zhang University of Technology Sydney

DOI:

https://doi.org/10.1609/aaai.v40i1.37008

Abstract

Single-cell RNA sequencing (scRNA-seq) is essential for decoding tumor heterogeneity. However, pan-cancer research still faces two key challenges: learning discriminative and efficient single-cell representations, and establishing a comprehensive evaluation benchmark. In this paper, we introduce \algoname, a lightweight hybrid neural network that combines the strengths of Transformers and state-space models to achieve a balance between performance and efficiency. \algoname consists of a front-end local-context encoder with shared self-attention layers to capture complex, order-independent gene interactions; and a back-end global sequential feature decoder that efficiently integrates global context using a linear-time state-space model. This modular design preserves the expressive power of Transformers while leveraging the scalability of Mamba to enable transcriptome modeling, effectively capturing both local and global regulatory signals. To enable robust evaluation, we also construct a large-scale pan-cancer single-cell benchmark, \algoname Bench, containing over 3.5 million high-quality cells across 33 cancer subtypes, curated through a rigorous preprocessing pipeline. Experimental results show that \algoname outperforms state-of-the-art models on our pan-cancer benchmark (+4.0\%) and across multiple public tasks, including cell type annotation (+7.4\%), batch integration (+4.0\%) and multi-omics integration (+3.1\%).

How to Cite

Huang, X., Zhu, T., Zuo, Y., Xia, X., Wu, Z., Yan, J., … Zhang, J. (2026). PanFoMa: A Lightweight Foundation Model and Benchmark for Pan-Cancer. Proceedings of the AAAI Conference on Artificial Intelligence, 40(1), 453–461. https://doi.org/10.1609/aaai.v40i1.37008

Issue

Section

AAAI Technical Track on Application Domains I

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