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WorMachine: machine learning-based phenotypic analysis tool for worms
- Source :
- BMC Biology, Vol 16, Iss 1, Pp 1-11 (2018)
- Publication Year :
- 2018
- Publisher :
- BMC, 2018.
-
Abstract
- 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 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.
Details
- Language :
- English
- ISSN :
- 17417007
- Volume :
- 16
- Issue :
- 1
- Database :
- Directory of Open Access Journals
- Journal :
- BMC Biology
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.73938c4876d348588673c6f5dce749d8
- Document Type :
- article
- Full Text :
- https://doi.org/10.1186/s12915-017-0477-0