ArticleNature communications2020
Deep learning-assisted comparative analysis of animal trajectories with DeepHL.
Article in Nature communications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Tribolium castaneum with longer duration of tonic immobility have more variations corresponding to the human Parkinson's disease genomic region.Scientific reports · 2026Article
- A new data-driven paradigm for the study of avian migratory navigation.Movement ecology · 2025Review
- Development of automatic insect-tracking robot system for measuring local activity changes in free walking.Frontiers in robotics and AI · 2025Article
- A multidimensional approach to the self in non-human animals through the Pattern Theory of Self.Frontiers in psychology · 2025Article
- A comparison of machine learning methods for quantifying self-grooming behavior in mice.Frontiers in behavioral neuroscience · 2024Article
- Ponds as experimental arenas for studying animal movement: current research and future prospects.Movement ecology · 2023Review
- Bench to bedside - new insights into the pathogenesis of necrotizing enterocolitis.Nature reviews. Gastroenterology & hepatology · 2022Review
- Deep inference of seabird dives from GPS-only records: Performance and generalization properties.PLoS computational biology · 2022Article
- A predictive flight-altitude model for avoiding future conflicts between an emblematic raptor and wind energy development in the Swiss Alps.Royal Society open science · 2022Article
- Advancements in Genomic and Behavioral Neuroscience Analysis for the Study of Normal and Pathological Brain Function.Frontiers in molecular neuroscience · 2022Review
- Cross-species behavior analysis with attention-based domain-adversarial deep neural networks.Nature communications · 2021Article
- Unraveling hidden interactions in complex systems with deep learning.Scientific reports · 2021Article
- Beyond Single Discrete Responses: An Integrative and Multidimensional Analysis of Behavioral Dynamics Assisted by Machine Learning.Frontiers in behavioral neuroscience · 2021Article
- Deep learning-assisted comparative analysis of animal trajectories with DeepHL.Nature communications · 2020Article
Corrections and comments
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Authors and funding
18 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A comparative analysis of animal behavior (e.g., male vs. female groups) has been widely used to elucidate behavior specific to one group since pre-Darwinian times. However, big data generated by new sensing technologies, e.g., GPS, makes it difficult for them to contrast group differences manually. This study introduces DeepHL, a deep learning-assisted platform for the comparative analysis of animal movement data, i.e., trajectories. This software uses a deep neural network based on an attention mechanism to automatically detect segments in trajectories that are characteristic of one group. It then highlights these segments in visualized trajectories, enabling biologists to focus on these segments, and helps them reveal the underlying meaning of the highlighted segments to facilitate formulating new hypotheses. We tested the platform on a variety of trajectories of worms, insects, mice, bears, and seabirds across a scale from millimeters to hundreds of kilometers, revealing new movement features of these animals.
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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.