most citedBenchmarking virtual cell models for in-the-wild perturbation response

1 citations · 1 across the 5 of their papers we have counts for

collaborators

5 papers

q-bio.CB2026★ 1 cited

Benchmarking virtual cell models for in-the-wild perturbation response

Xinjie Mao, Songming Zhang, Qianhong Wen +10

Virtual cell (VC) models aim to predict cellular responses to any perturbations in silico and have emerged as a promising approach for drug discovery and precision medicine. Yet, a…

cs.AI2026

AblateCell: A Reproduce-then-Ablate Agent for Virtual Cell Repositories

Xue Xia, Chengkai Yao, Mingyu Tsoi +10

Systematic ablations are essential to attribute performance gains in AI Virtual Cells, yet they are rarely performed because biological repositories are under-standardized and tigh…

cs.AI2026

HarmonyCell: Automating Single-Cell Perturbation Modeling under Semantic and Distribution Shifts

Wenxuan Huang, Mingyu Tsoi, Yanhao Huang +10

Single-cell perturbation studies face dual heterogeneity bottlenecks: (i) semantic heterogeneity--identical biological concepts encoded under incompatible metadata schemas across d…

cs.LG2026

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

Shuizhou Chen, Lang Yu, Xueqin Lin +12

Virtual-cell models aim to predict how cell populations respond to perturbations, but control and treated cells are measured as unpaired populations, complicating the learning of p…

q-bio.QM2025

Accurate de novo sequencing of the modified proteome with OmniNovo

Yuhan Chen, Shang Qu, Zhiqiang Gao +13

Post-translational modifications (PTMs) serve as a dynamic chemical language regulating protein function, yet current proteomic methods remain blind to a vast portion of the modifi…