WorMachine: Machine learning-based phenotypic analysis tool for worms

Adam Hakim*, Yael Mor, Itai Antoine Toker, Amir Levine, Moran Neuhof, Yishai Markovitz, Oded Rechavi

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


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 suitable for analysis of a variety of biological questions and provides an accurate and reproducible analysis tool for measuring diverse phenotypes. It serves as a "quick and easy," convenient, high-throughput, and automated solution for nematode research.

Original languageEnglish
Article number8
JournalBMC Biology
Issue number1
StatePublished - 16 Jan 2018


  • Caenorhabditis elegans
  • Deep learning
  • Feature extraction
  • High-throughput image analysis
  • Image processing
  • Machine learning
  • Phenotype analysis


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