A highly efficient, scalable pipeline for fixed feature extraction from large-scale high-content imaging screens.

ABSTRACT: Applying artificial intelligence (AI) to image-based morphological profiling cells offers significant potential for identifying disease states and drug responses in high-content imaging (HCI) screens. When differences between populations (e.g., healthy vs. diseased) are unknown or imperceptible to the human eye, large-scale HCI screens are essential, providing numerous replicates to build reliable models and accounting for confounding factors like donor and intra-experimental variations. As screen sizes grow, so does the challenge of analyzing high-dimensional datasets in an efficient way while preserving interpretable features and predictive power. Here, we introduce ScaleFEx℠, a memory-efficient, open-source Python pipeline that extracts biologically meaningful features from HCI datasets

SUBMITTER: Comolet G 

PROVIDER: S-EPMC11667173 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature