2 citations · 2 across the 8 of their papers we have counts for
6 papers · 1 filter
PerturbBridge: Direct Phenotypic Supervision for Cellular Perturbation Generation
Yucheng Yang, Xiaoxiao Sun, Huangqingbo Sun +3
Virtual cell models should capture phenotypes induced by specific perturbations. However, image-based models are typically trained and evaluated to match real-image distributions,…
PerturbCellRL: Aligning Distributions and Grounding Biology via Post-Training Perturbation Generators
Dongxia Wu, Mingyu Li, Yuhui Zhang +4
Single-cell perturbation models can reduce costly wet-lab screening by predicting how cells respond transcriptionally to interventions. Recent advances in flow-matching have enable…
CellFluxRL: Biologically-Constrained Virtual Cell Modeling via Reinforcement Learning
Dongxia Wu, Shiye Su, Yuhui Zhang +4
Building virtual cells with generative models to simulate cellular behavior in silico is emerging as a promising paradigm for accelerating drug discovery. However, prior image-base…
Uncertainty-Aware Distribution-to-Distribution Flow Matching for Scientific Imaging
Dongxia Wu, Yuhui Zhang, Serena Yeung-Levy +2
Distribution-to-distribution generative models support scientific imaging tasks ranging from modeling cellular perturbation responses to translating medical images across condition…
iSight: Towards expert-AI co-assessment for improved immunohistochemistry staining interpretation
Jacob S. Leiby, Jialu Yao, Pan Lu +17
Immunohistochemistry (IHC) provides information on protein expression in tissue sections and is commonly used to support pathology diagnosis and disease triage. While AI models for…
PaperSearchQA: Learning to Search and Reason over Scientific Papers with RLVR
James Burgess, Jan N. Hansen, Duo Peng +5
Search agents are language models (LMs) that reason and search knowledge bases (or the web) to answer questions; recent methods supervise only the final answer accuracy using reinf…