arXiv AI August 10, 2026 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

Control-Anchored Residual Flow Matching Conditioned on Gene Geometry for Virtual Cell Perturbation Modeling

Read the original on arXiv AI →

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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv AI

Jun 12

OCOO-T : A Simple and Scalable Virtual Cell Model for Transcriptional Perturbation Response Prediction

arXiv:2606. 12838v1 Announce Type: cross Abstract: Predicting single-cell transcriptional responses to genetic, chemical and cytokine perturbations is a fundamental challenge in computational biology and AI Virtual Cell (AIVC) modeling, with direct implications for drug discovery and the elucidation of gene regulatory networks.

By Danning Jiang, Zheming An, Yalong Zhao, Lipeng Lai

llms rag benchmarks

arXiv AI

Aug 18

$D^{2}R^{2}$: Discrete Diffusion with Regulation Reinforcement for Single-Cell Perturbation Prediction

arXiv:2608. 15288v1 Announce Type: new Abstract: Predicting single-cell transcriptomic responses to genetic perturbations is central to functional genomics and virtual-cell modeling.

By Ninghan Fan, Qi Liu, Xunuo Zhu, Yukai Sun, Luyuan Chen, Xuheng Zhou, Yuetian Du, Ming Kong, Xiaojun Zhu, Jie Liu, Zhan Zhou, Qiang Zhu

diffusion

arXiv Machine Learning

Jun 25

Stable-Shift: Biologically Structured Prediction of Transcriptional Responses to Unseen Gene Perturbations

arXiv:2606. 24940v1 Announce Type: cross Abstract: Predicting transcriptional responses to genetic perturbations could reduce the experimental burden of functional genomics, but extrapolation to genes that were never perturbed during training remains difficult.

By Sajib Acharjee Dip, Liqing Zhang

benchmarks

arXiv Machine Learning

Aug 7

BioM-JEPA: joint-embedding prediction of graph-connected gene blocks in single cells

arXiv:2608. 05928v1 Announce Type: new Abstract: Single-cell transcriptomes are sparse observations of coordinated biological programmes, yet most self-supervised models learn by reconstructing individual genes.

By Yuhao Wang, Zelin Zang, Yuxuan Liu, Zhen Lei, Stan Z. Li

rag fine-tuning efficiency

arXiv AI

Sep 2

SCALE:Scalable Conditional Atlas-Level Endpoint transport for virtual cell perturbation prediction

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 Machine Learning

Jun 29

PerturbCellRL: Verifier-Guided Reinforcement Learning for Single-Cell Perturbation Prediction

arXiv:2606. 27752v1 Announce Type: new Abstract: Single-cell perturbation models can reduce costly wet-lab screening by predicting how cells respond transcriptionally to interventions.

By Dongxia Wu, Mingyu Li, Yuhui Zhang, Anurendra Kumar, Emma Lundberg, Serena Yeung-Levy, Emily B. Fox

reinforcement-learning benchmarks safety

One email a morning, machine-written

Related stories

OCOO-T : A Simple and Scalable Virtual Cell Model for Transcriptional Perturbation Response Prediction

$D^{2}R^{2}$: Discrete Diffusion with Regulation Reinforcement for Single-Cell Perturbation Prediction

Stable-Shift: Biologically Structured Prediction of Transcriptional Responses to Unseen Gene Perturbations

BioM-JEPA: joint-embedding prediction of graph-connected gene blocks in single cells

SCALE:Scalable Conditional Atlas-Level Endpoint transport for virtual cell perturbation prediction

PerturbCellRL: Verifier-Guided Reinforcement Learning for Single-Cell Perturbation Prediction