Scoring diverse cellular morphologies in image-based screens with iterative feedback and machine learning.

ABSTRACT: Many biological pathways were first uncovered by identifying mutants with visible phenotypes and by scoring every sample in a screen via tedious and subjective visual inspection. Now, automated image analysis can effectively score many phenotypes. In practical application, customizing an image-analysis algorithm or finding a sufficient number of example cells to train a machine learning algorithm can be infeasible, particularly when positive control samples are not available and the phenotype of interest is rare. Here we present a supervised machine learning approach that uses iterative feedback to readily score multiple subtle and complex morphological phenotypes in high-throughput, image-based screens. First, automated cytological profiling extracts hundreds of numerical descriptors for

SUBMITTER: Jones TR 

PROVIDER: S-EPMC2634799 | biostudies-literature | 2009 Feb

REPOSITORIES: biostudies-literature