ArticlePatterns (New York, N.Y.)2025
Evaluation of unsupervised learning algorithms for the classification of behavior from pose estimation data.
Article in Patterns (New York, N.Y.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- Integrating multidimensional nociceptive-related cortical features for unsupervised assessment of anesthesia states in rats.iScience · 2026Article
- Evaluation of unsupervised learning algorithms for the classification of behavior from pose estimation data.Patterns (New York, N.Y.) · 2025Article
- The impact of machine learning on ethological neuroscience.Frontiers in behavioral neuroscience · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Analyzing animal behavior is crucial for decoding brain function, modeling neurological disorders, and assessing therapeutics. Recent advances in pose-estimation tools like DeepLabCut and SLEAP have revolutionized behavioral analysis by enabling precise tracking of animal body movements. However, these tools do not automate behavioral classification. Unsupervised learning algorithms address this gap by identifying clusters of recurring behavioral motifs from pose-tracking data without requiring pre-labeled datasets, reducing observer bias and uncovering novel patterns. This study compares four recent unsupervised learning algorithms-B-SOiD, BFA, VAME, and Keypoint-MoSeq-analyzing their methodological approaches, clustering efficiency, and ability to produce meaningful behavioral classifications. By offering a qualitative and quantitative evaluation, this paper aims to aid researchers in selecting the most suitable tool for their specific research needs.
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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.