WorMachine: machine learning-based phenotypic analysis tool for worms.

ABSTRACT:

Background

Caenorhabditis elegans nematodes are powerful model organisms, yet quantification of visible phenotypes is still often labor-intensive, biased, and error-prone. We developed WorMachine, a three-step MATLAB-based image analysis software that allows (1) automated identification of C. elegans worms, (2) extraction of morphological features and quantification of fluorescent signals, and (3) machine learning techniques for high-level analysis.

Results

We examined the power of WorMachine using five separate representative assays: supervised classification of binary-sex phenotype, scoring continuous-sexual phenotypes, quantifying the effects of two different RNA interference treatments, and measuring intracellular protein aggregation.

Conclusions

WorMachine is sui

SUBMITTER: Hakim A 

PROVIDER: S-EPMC5769209 | biostudies-literature | 2018 Jan

REPOSITORIES: biostudies-literature