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

Journal: BMC biology
Published Date: Jan 16, 2018

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.

Authors

  • Adam Hakim
    Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel. adamhakim@mail.tau.ac.il.
  • Yael Mor
    Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel. yaelmor@mail.tau.ac.il.
  • Itai Antoine Toker
    Department of Neurobiology, Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv, Israel.
  • Amir Levine
    Department of Biochemistry and Molecular Biology, Institute for Medical Research Israel-Canada (IMRIC), School of Medicine, The Hebrew University of Jerusalem, Jerusalem, Israel.
  • Moran Neuhof
    Department of Neurobiology, Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv, Israel.
  • Yishai Markovitz
    Department of Neurobiology, Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv, Israel.
  • Oded Rechavi
    Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel. odedrechavi@gmail.com.

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