Yuge Ji

The paper presents a longitudinal multimodal benchmarking dataset and competition to evaluate modality prediction methods for gene regulatory inference, showing top models outperform existing approaches.

Pertpy provides a modular framework for analyzing large-scale perturbation single-cell experiments, offering tools for metadata annotation and perturbation distance calculations.

Prophet predicts cellular phenotypes from perturbations using transfer learning, outperforming baselines and enabling in silico screening for therapeutic discovery.

The paper evaluates distance metrics for single-cell RNA-seq data and recommends mean squared error for comparing populations and evaluating gene expression models.

The compositional perturbation autoencoder separates drug, dose and cell-type effects in single-cell transcriptomes, predicts unseen combinations, and generates 5,329 unmeasured genetic combinations in silico.