Proceedings

CycSeq: Leveraging Cyclic Data Generation for Accurate Perturbation Prediction in Single-Cell RNA-Seq

Yicheng Liu, Sai Wu, Tianyun Zhang, Chang Yao, Ning Shen

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Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence. AI4Tech: AI Enabling Technologies. Pages 9277-9285.

https://doi.org/10.24963/ijcai.2025/1031

Keywords

  • Domain-specific AI4Tech: AI4Care and AI4Health
  • Domain-specific AI4Tech: AI4Biotech
  • Domain-specific AI4Tech: AI4Medicine and AI4Drug
  • Emerging AI4Tech: Emerging AI4Tech areas

Abstract

Understanding and predicting the effects of cellular perturbations using single-cell sequencing technology remains a critical and challenging problem in biotechnology. In this work, we introduce CycSeq, a deep learning framework that leverages cyclic data generation and recent advances in neural architectures to predict single-cell responses under specified perturbations across multiple cell lines, while also generating the corresponding single-cell expression profiles. Specifically, CycSeq addresses the challenge of learning heterogeneous perturbation responses from unpaired single-cell gene expression data by generating pseudo-pairs through cyclic data generation. Experimental results demonstrate that CycSeq outperforms existing methods in perturbation prediction tasks, as evaluated using computational metrics such as R-squared and MAE. Furthermore, CycSeq employs a unified architecture that integrates information from multiple cell lines, enabling robust predictions even for long-tail cell lines with limited training data. The source code is publicly available at https://github.com/yczju/cycseq.

Yicheng Liu, Sai Wu, Tianyun Zhang, Chang Yao, and Ning Shen. 2025. CycSeq: Leveraging Cyclic Data Generation for Accurate Perturbation Prediction in Single-Cell RNA-Seq. In Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-25. IJCAI, 9277–9285. https://doi.org/10.24963/ijcai.2025/1031
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