Learning representations for image-based profiling of perturbations.

ABSTRACT: Measuring the phenotypic effect of treatments on cells through imaging assays is an efficient and powerful way of studying cell biology, and requires computational methods for transforming images into quantitative data. Here, we present an improved strategy for learning representations of treatment effects from high-throughput imaging, following a causal interpretation. We use weakly supervised learning for modeling associations between images and treatments, and show that it encodes both confounding factors and phenotypic features in the learned representation. To facilitate their separation, we constructed a large training dataset with images from five different studies to maximize experimental diversity, following insights from our causal analysis. Training a model with this dataset suc

SUBMITTER: Moshkov N 

PROVIDER: S-EPMC10881515 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature