ArticleBMC biology2018
WorMachine: machine learning-based phenotypic analysis tool for worms.
Article in BMC biology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
27 citing papers in PubMed.
- Peptone-free nematode growth medium improves culture stability and physiological resilience in Caenorhabditis elegans.Biogerontology · 2026Article
- Large vision model framework for automated C. elegans analysis: From static morphometry to dynamic neural activity.PLoS computational biology · 2026Article
- WormSpot: a machine learning-powered viability scoring platform inMicrobiology spectrum · 2026Article
- Scavenger Cells Failure to Maintain Systemic RNA Homeostasis Causes Epigenetically Inherited Germline Tumors.bioRxiv : the preprint server for biology · 2026Article
- Identification of natural products and synthetic analogs which inhibit microsporidia spores and prevent infection.Journal of invertebrate pathology · 2026Article
- High-Throughput Toxicity Screening withEnvironmental science & technology · 2026Review
- A deep learning framework for species-level identification of root-knot nematodes using perineal pattern images.Scientific reports · 2026Article
- Short-term L1 arrest duration does not significantly alter organismal phenotypes in synchronizedmicroPublication biology · 2026Article
- HarnessingSensors (Basel, Switzerland) · 2025Review
- Small-molecule screen in C. elegans identifies benzenesulfonamides as inhibitors of microsporidia spores.npj antimicrobials and resistance · 2025Article
- Screening of the Pandemic Response Box identifies anti-microsporidia compounds.PLoS neglected tropical diseases · 2023Article
- Article
- Automation ofComputational and structural biotechnology journal · 2023Article
- High-throughput small molecule screen identifies inhibitors of microsporidia invasion and proliferation in C. elegans.Nature communications · 2022Article
- Sexual morph specialisation in a trioecious nematode balances opposing selective forces.Scientific reports · 2022Article
- Transgenerational inheritance of sexual attractiveness via small RNAs enhances evolvability in C. elegans.Developmental cell · 2022Article
- Deep learning is widely applicable to phenotyping embryonic development and disease.Development (Cambridge, England) · 2021Article
- Mask R-CNN Based C. Elegans Detection with a DIY Microscope.Biosensors · 2021Article
- Towards Lifespan Automation forSensors (Basel, Switzerland) · 2021Article
- Natural genetic variation drives microbiome selection in the Caenorhabditis elegans gut.Current biology : CB · 2021Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
backgroundCaenorhabditis 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.
resultsWe 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.
conclusionsWorMachine 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.