Michael Bornholdt

A versatile information retrieval framework for evaluating profile strength and similarity

The paper introduces a statistical framework using mean average precision (mAP) to evaluate phenotypic activity and consistency in high-dimensional profiling data across various modalities and perturbation types.

Learning representations for image-based profiling of perturbations

The paper develops a weakly supervised learning method to learn treatment effect representations from imaging data, improving downstream analysis performance.

Morphology and gene expression profiling provide complementary information for mapping cell state

The paper compares morphology and gene expression data to determine their complementary roles in mapping cell states across drug perturbations.