ArticlePLoS computational biology2022
Deep learning for robust and flexible tracking in behavioral studies for C. elegans.
Article in PLoS computational biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
27 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Systematic review of artificial intelligence use in behavioral analysis of invertebrate and larval model organisms: methods, applications and future recommendations.Frontiers in artificial intelligence · 2026Pooled it
- Machine Learning-Accelerated Analysis ofResearch square · 2026Article
- Large vision model framework for automated C. elegans analysis: From static morphometry to dynamic neural activity.PLoS computational biology · 2026Article
- Deep Convolutional Neural Networks for Autofocus Control on aBiosensors · 2026Article
- High-Throughput Toxicity Screening withEnvironmental science & technology · 2026Review
- A comprehensive review of artificial intelligence as a catalyst in aging research: insights, gaps and future perspectives.Frontiers in aging · 2026Review
- Temporal Analysis of Embryonic Epidermal Morphogenesis inInternational journal of molecular sciences · 2025Article
- Automated C. elegans behavior analysis via deep learning-based detection and tracking.PLoS computational biology · 2025Article
- Article
- Article
- WormRACER: Robust Analysis by Computer-Enhanced Recording.GeroScience · 2025Article
- Network Flow Method Integrates Skeleton Information for MultipleSensors (Basel, Switzerland) · 2025Article
- Machine learning-based analysis of microfluidic device immobilized C. elegans for automated developmental toxicity testing.Scientific reports · 2025Article
- Automated multimodal imaging of Caenorhabditis elegans behavior in multi-well plates.Genetics · 2024Article
- WormCNN-Assisted Establishment and Analysis of Glycation Stress Models inInternational journal of molecular sciences · 2024Article
- vivoBodySeg: Machine learning-based analysis of C. elegans immobilized in vivoChip for automated developmental toxicity testing.Research square · 2024Article
- Analysis of Varroa Mite Colony Infestation Level Using New Open Software Based on Deep Learning Techniques.Sensors (Basel, Switzerland) · 2024Article
- Fast detection of slender bodies in high density microscopy data.Communications biology · 2023Article
- Automated scoring of nematode nictation on a textured background.bioRxiv : the preprint server for biology · 2023Article
- A robotic system for automated genetic manipulation and analysis ofPNAS nexus · 2023Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Robust and accurate behavioral tracking is essential for ethological studies. Common methods for tracking and extracting behavior rely on user adjusted heuristics that can significantly vary across different individuals, environments, and experimental conditions. As a result, they are difficult to implement in large-scale behavioral studies with complex, heterogenous environmental conditions. Recently developed deep-learning methods for object recognition such as Faster R-CNN have advantages in their speed, accuracy, and robustness. Here, we show that Faster R-CNN can be employed for identification and detection of Caenorhabditis elegans in a variety of life stages in complex environments. We applied the algorithm to track animal speeds during development, fecundity rates and spatial distribution in reproductive adults, and behavioral decline in aging populations. By doing so, we demonstrate the flexibility, speed, and scalability of Faster R-CNN across a variety of experimental conditions, illustrating its generalized use for future large-scale behavioral studies.
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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.