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Proceedings of the AAAI Conference on Artificial Intelligence

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CLM-Access: A Specialized Foundation Model for High-Dimen...
Ziqiang Liu, · 2026-03-14 · via Proceedings of the AAAI Conference on Artificial Intelligence

Authors

  • Ziqiang Liu Hangzhou Institute of Medicine, Chinese Academy of Sciences
  • Bowen Li Hangzhou Institute of Medicine, Chinese Academy of Sciences
  • Zhenyu Xu Hangzhou Institute of Medicine, Chinese Academy of Sciences Faculty of Health Science, University of Macau
  • Yantao Li Hangzhou Institute of Medicine, Chinese Academy of Sciences
  • Junwei Zhang Hangzhou Institute of Medicine, Chinese Academy of Sciences
  • Chulin Sha Hangzhou Institute of Medicine, Chinese Academy of Sciences
  • Xiaolin Li Hangzhou Institute of Medicine, Chinese Academy of Sciences

DOI:

https://doi.org/10.1609/aaai.v40i1.37046

Abstract

Inspired by the success of large language models (LLMs) in natural language processing, cell language models (CLMs) have emerged as a promising paradigm to learn cell representations from high-dimensional single-cell data—particularly transcriptomic profiles from scRNA-seq. These foundation models have shown remarkable potential across a variety of downstream applications. However, there remains a lack of foundation models for scATAC-seq data, which measures chromatin accessibility at single-cell level and is critical for decoding epigenetic regulation. Developing such model is considerably more challenging due to the unique characteristics of scATAC-seq data, including the vast number of chromatin regions, lack of standardized annotations, extreme sparsity, and near-binary distributions. To address these challenges, we systematically explore various strategies and propose CLM-Access, a specialized foundation model for scATAC-seq data. CLM-Access incorporates three main innovations: (1) an unified data processing pipeline that maps 2.8 million cells onto an unified reference of over 1 million chromatin regions; (2) a specialized patching and embedding strategy to effectively manage high-dimensional inputs; and (3) a tailored masking and loss function design that preserves fine-grained regional information while enhancing training efficiency and representation quality. With comprehensive benchmarks, we show that CLM-Access significantly outperforms existing methods in key downstream tasks, including batch effect correction, cell type annotation, RNA expression prediction, and multi-modal integration. This work establishes a scalable and interpretable foundation model for single-cell epigenomic analysis and expands the application of CLMs in single-cell research.

How to Cite

Liu, Z., Li, B., Xu, Z., Li, Y., Zhang, J., Sha, C., & Li, X. (2026). CLM-Access: A Specialized Foundation Model for High-Dimensional Single-Cell ATAC-Seq Analysis. Proceedings of the AAAI Conference on Artificial Intelligence, 40(1), 791–799. https://doi.org/10.1609/aaai.v40i1.37046

Issue

Section

AAAI Technical Track on Application Domains I

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