{"type":"ScholarlyArticle","title":"CycSeq: Leveraging Cyclic Data Generation for Accurate Perturbation Prediction in Single-Cell RNA-Seq","authors":[{"name":"Yicheng Liu","slug":"yicheng-liu","given":"Yicheng","family":"Liu","affiliation":""},{"name":"Sai Wu","slug":"sai-wu","given":"Sai","family":"Wu","affiliation":""},{"name":"Tianyun Zhang","slug":"tianyun-zhang","given":"Tianyun","family":"Zhang","affiliation":""},{"name":"Chang Yao","slug":"chang-yao","given":"Chang","family":"Yao","affiliation":""},{"name":"Ning Shen","slug":"ning-shen","given":"Ning","family":"Shen","affiliation":""}],"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.","doi":"10.24963/ijcai.2025/1031","url":"https://ijcai.org/proceedings/2025/1031","pdf":"https://ijcai.org/proceedings/2025/1031.pdf","markdown":"https://ijcai.org/proceedings/2025/1031.md","bibtex":"https://ijcai.org/proceedings/2025/bibtex/1031","volume":{"slug":"2025","title":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2025","url":"https://ijcai.org/proceedings/2025/","year":2025,"location":"Montreal, Canada","dates":"16-22 August 2025","editors":"James Kwok","isbn":"978-1-956792-06-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2025"},"section":"AI4Tech: AI Enabling Technologies","track":"AI4Tech: AI Enabling Technologies","number":1031,"pages":{"first":9277,"last":9285},"keywords":["Domain-specific AI4Tech: AI4Care and AI4Health","Domain-specific AI4Tech: AI4Biotech","Domain-specific AI4Tech: AI4Medicine and AI4Drug","Emerging AI4Tech: Emerging AI4Tech areas"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}