arXiv:2608.23114v1 Announce Type: cross
Abstract: Predicting transcriptome-wide responses to unseen genetic perturbations remains a major computational challenge because accurate prediction requires...
By Jiawen Liu, Xuechenxiao Cao, Yutong Li, Bing Liu, Jiaming Liang, Tinghe Zhang, Xiaoqi Sheng, Hongmin Cai
SCALE is a conditional transport model that treats cells as unordered sets to predict treated cell populations without requiring cell-level matching. It uses a shared set-aware encoder and a conditional DiT backbone to learn latent transport, enabling endpoint supervision that is directly delta-aligned. Across diverse perturbation types—including genetic, chemical, developmental, and immune—SCALE accurately recovers gene‑expression changes, response directions, and population structure, outperforming competing methods on CRISPR data and successfully prioritizing cytokines that elicit distinct immune responses.
By Shuizhou Chen, Lang Yu, Xueqin Lin, Xinjie Mao, Songming Zhang, Xinyu Gu, Hao Wu, Sheng Xu, Kedu Jin, Lei Bai, Quan Qian, Qin Chen, Qiang Gao, Siqi Sun, Zhangyang Gao
arXiv:2608. 06824v1 Announce Type: cross Abstract: A central task in virtual cell modeling is predicting single-cell transcriptional responses to unseen genetic perturbations and drug combinations, and biological networks provide valuable priors on gene relationships.
By Quanquan Li, Yihe Chi, Liuyang Song, Hongbo Zhang, Jingyu Li, Xidong Xi, Conghua Wei, Yijie Sun, Yu Chen, Xin Liu, Qi Hu, Jing Ke, Guitao Cao
PopPert is a framework that models population-level joint gene expression distributions to predict transcriptional responses to perturbations in single-cell RNA sequencing data. By using a low‑rank Gaussian Copula, it captures gene co‑expression patterns and eliminates the need for cell‑to‑cell correspondence, thereby reducing sensitivity to single‑cell noise. Across multiple benchmarks, PopPert outperforms existing methods in differential expression recovery, perturbation effect estimation, and distribution matching, demonstrating the effectiveness of population‑level joint distribution learning for unpaired single‑cell data.
By Handong Wang, Jiaxin Qi, Haochen Feng, Baisheng Lai
arXiv:2607. 23447v1 Announce Type: new Abstract: Predicting cellular responses to unseen chemical perturbations is challenging due to unknown targets and mechanisms, high-dimensional expression responses, and limited experimental coverage of the large small-molecule design space.
By Yuche Gao, Jos\'e Miguel Hern\'andez-Lobato, Siyuan Guo
arXiv:2602. 04901v2 Announce Type: replace-cross Abstract: Predicting transcriptional responses to genetic perturbations is a central problem in functional genomics.
By Jiafa Ruan, Ruijie Quan, Liyang Xu, Zongxin Yang, Yi Yang