activity
20202026
most citedA Survey on Generative Diffusion Model

67 citations · 373 across the 64 of their papers we have counts for

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Showing 2026Show all

6 papers · 1 filter

cs.DB2026

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…

cs.LG2026

E-OPSD: Taming Entropy Overshoot in On-Policy Self-Distillation

Yifei Liu, Minghao Fang, Xinyu Gu +6

On-policy self-distillation (OPSD) provides dense token-level supervision without a second model: one network acts as teacher with the reference solution and as student with only t…

cs.CL2026

Accurate, Interdisciplinary and Transparent Structure-property Understanding with Deep Native Structural Reasoning

Chen Tang, Yizhou Wang, Jianyu Wu +26

Structure-property relationships are foundational to biology, chemistry and materials science, where function, reactivity and physical response emerge from spatial, chemical and pe…

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.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…