ArticleMolecular pain
Machine learning-based automated phenotyping of inflammatory nocifensive behavior in mice.
Article in Molecular pain. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
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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
13 citing papers in PubMed, 21 citations in OpenAlex.
- Deep Learning-Based Quantification of Spontaneous Pain Behaviours in Mice.European journal of pain (London, England) · 2026Article
- Visual detection of seizures in mice using supervised machine learning.Cell reports methods · 2025Article
- A machine learning tool with light-based image analysis for automatic classification of 3D pain behaviors.Cell reports methods · 2025Article
- A deep learning-based system for automatic detection of emesis with high accuracy in Suncus murinus.Communications biology · 2025Article
- A Survey on AI-Driven Mouse Behavior Analysis Applications and Solutions.Bioengineering (Basel, Switzerland) · 2024Review
- Visual detection of seizures in mice using supervised machine learning.bioRxiv : the preprint server for biology · 2024Article
- An accurately supervised motion-aware deep network for non-contact pain assessment of trigeminal neuralgia mouse model.Journal of oral & facial pain and headache · 2024Article
- A comparison of machine learning methods for quantifying self-grooming behavior in mice.Frontiers in behavioral neuroscience · 2024Article
- The International Mouse Phenotyping Consortium: comprehensive knockout phenotyping underpinning the study of human disease.Nucleic acids research · 2023Article
- Single-Cell RNA Sequencing with Spatial Transcriptomics of Cancer Tissues.International journal of molecular sciences · 2022Review
- Pleiotropy data resource as a primer for investigating co-morbidities/multi-morbidities and their role in disease.Mammalian genome : official journal of the International Mammalian Genome Society · 2022Article
- Machine learning and deep learning frameworks for the automated analysis of pain and opioid withdrawal behaviors.Frontiers in neuroscience · 2022Review
- Impact of Dose, Sex, and Strain on Oxaliplatin-Induced Peripheral Neuropathy in Mice.Frontiers in pain research (Lausanne, Switzerland) · 2021Article
Corrections and comments
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Authors and funding
8 authors at 1 institution in 1 country.
Funding
Abstract
The discovery and development of new and potentially nonaddictive pain therapeutics requires rapid, yet clinically relevant assays of nociception in preclinical models. A reliable and scalable automated scoring system for nocifensive behavior of mice in the formalin assay would dramatically lower the time and labor costs associated with experiments and reduce experimental variability. Here, we present a method that exploits machine learning techniques for video recordings that consists of three components: key point detection, per frame feature extraction using these key points, and classification of behavior using the GentleBoost algorithm. This approach to automation is flexible as different model classifiers or key points can be used with only small losses in accuracy. The adopted system identified the behavior of licking/biting of the hind paw with an accuracy that was comparable to a human observer (98% agreement) over 111 different short videos (total 284 min) at a resolution of 1 s. To test the system over longer experimental conditions, the responses of two inbred strains, C57BL/6NJ and C57BL/6J, were recorded over 90 min post formalin challenge. The automated system easily scored over 80 h of video and revealed strain differences in both response timing and amplitude. This machine learning scoring system provides the required accuracy, consistency, and ease of use that could make the formalin assay a feasible choice for large-scale genetic 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.