most citedEfficient Fine-Tuning of Single-Cell Foundation Models Enables Zero-Shot Molecular Perturbation Prediction

2 citations · 3 across the 5 of their papers we have counts for

collaborators

5 papers

q-bio.QM2025

HypoGeneAgent: A Hypothesis Language Agent for Gene-Set Cluster Resolution Selection Using Perturb-seq Datasets

Ying Yuan, Xing-Yue Monica Ge, Aaron Archer Waterman +8

Large-scale single-cell and Perturb-seq investigations routinely involve clustering cells and subsequently annotating each cluster with Gene-Ontology (GO) terms to elucidate the un…

cs.AI2025

Contextualizing biological perturbation experiments through language

Menghua Wu, Russell Littman, Jacob Levine +4

High-content perturbation experiments allow scientists to probe biomolecular systems at unprecedented resolution, but experimental and analysis costs pose significant barriers to w…

cs.LG2025★ 1 cited

Supervised Contrastive Block Disentanglement

Taro Makino, Ji Won Park, Natasa Tagasovska +11

Real-world datasets often combine data collected under different experimental conditions. This yields larger datasets, but also introduces spurious correlations that make it diffic…

cs.LG2024★ 2 cited

Efficient Fine-Tuning of Single-Cell Foundation Models Enables Zero-Shot Molecular Perturbation Prediction

Sepideh Maleki, Jan-Christian Huetter, Kangway V. Chuang +3

Predicting transcriptional responses to novel drugs provides a unique opportunity to accelerate biomedical research and advance drug discovery efforts. However, the inherent comple…

cs.CV2024

Weakly Supervised Set-Consistency Learning Improves Morphological Profiling of Single-Cell Images

Heming Yao, Phil Hanslovsky, Jan-Christian Huetter +2

Optical Pooled Screening (OPS) is a powerful tool combining high-content microscopy with genetic engineering to investigate gene function in disease. The characterization of high-c…