Fylo›ARCADIA›Graph
Hubs
Association·arcadia

Computational tools enable C. elegans behavioral phenotyping

Claim that computational methods and software facilitate the measurement and analysis of C. elegans behavioral phenotypes.

Confidence
70%
active

Evidence Quote

“Computational tools and software enable C. elegans behavioral phenotyping”

Relationship

Celegans computational tools enables C. elegans behavioral phenotyping

Arguments

Worm phenotyping workflowsubject
C. elegans behavioral phenotypingobject

Connections (3)

Reasoning: Computational tools enable behavioral phenotyping in C. elegansInferenceChain
Microchamber components enable microscopyAssociation
Reasoning: Image analysis tools and deep learning enable high-throughput worm phenotypingInferenceChain

Evidence

“Reference describing a database of C. elegans behavioral phenotypes.”

(2013). A database of Caenorhabditis elegans behavioral phenotypes doi:10.1038/nmeth.2560 ↗

“Reference on phenotyping C. elegans locomotion using SIFT.”

Koren Y et al. (2015). Model-Independent Phenotyping of C. elegans Locomotion Using Scale-Invariant Feature Transform doi:10.1371/journal.pone.0122326 ↗

“Reference describing Deep-Worm-Tracker, a deep learning tool for C. elegans behavioral tracking.”

Banerjee SC et al. (2022). Deep-Worm-Tracker: Deep Learning Methods for Accurate Detection and Tracking for Behavioral Studies in C. elegans doi:10.1101/2022.08.18.504475 ↗

“Reference on deep learning approach to flexible robust C. elegans behavioral tracking.”

Bates K et al. (2022). Deep learning for robust and flexible tracking in behavioral studies for C. elegans doi:10.1371/journal.pcbi.1009942 ↗

“Reference describing QuantWorm, a software suite for C. elegans phenotypic assays.”

Jung S-K et al. (2014). QuantWorm: A Comprehensive Software Package for Caenorhabditis elegans Phenotypic Assays doi:10.1371/journal.pone.0084830 ↗

“Reference on WormPose for image-based pose estimation in C. elegans.”

Hebert L et al. (2021). WormPose: Image synthesis and convolutional networks for pose estimation in C. elegans

“Reference on deep learning-based image recognition for nematode motility assays.”

Žofka M et al. (2022). Image recognition based on deep learning in Haemonchus contortus motility assays doi:10.1016/j.csbj.2022.05.014 ↗

“Reference for wrmXpress, a high-throughput image analysis package for worm assays.”

Wheeler NJ et al. (2022). wrmXpress: A modular package for high-throughput image analysis of parasitic and free-living worms doi:10.1371/journal.pntd.0010937 ↗

“Reference for scikit-image, a Python library for image processing.”

van der Walt S et al. (2014). scikit-image: image processing in Python doi:10.7717/peerj.453 ↗

“Reference for arcadiathemeR, a resource from Arcadia Science.”

Arcadia Science (2024). arcadiathemeR link ↗