71 citations · 72 across the 5 of their papers we have counts for
6 papers
CellChain: Benchmarking Cross-Scale Evidence Use for Perturbation Reasoning
Xue Xia, Yue Wang, Shuizhou Chen +6
Recent advances in foundation models and large-scale cellular perturbation datasets have driven growing interest in using LLMs to predict and reason about cellular responses. Biolo…
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…
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…
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…
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…
Robust FCC solute diffusion predictions from ab-initio machine learning methods
Henry Wu, Aren Lorenson, Ben Anderson +4
We evaluate the performance of four machine learning methods for modeling and predicting FCC solute diffusion barriers. More than 200 FCC solute diffusion barriers from previous de…