ArticleNature communications2021
Cross-species behavior analysis with attention-based domain-adversarial deep neural networks.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 11 citations in OpenAlex.
- Tribolium castaneum with longer duration of tonic immobility have more variations corresponding to the human Parkinson's disease genomic region.Scientific reports · 2026Article
- Cross-Species Framework for Emotional Well-Being and Brain Aging: Lessons From Behavioral Neuroscience.JAMA psychiatry · 2025Review
- Stereology neuron counts correlate with deep learning estimates in the human hippocampal subregions.Scientific reports · 2023Article
- Cross-species behavior analysis with attention-based domain-adversarial deep neural networks.Nature communications · 2021Article
- Cross-Species Behavioral Analysis via Multidimensional Knowledge Transfer.IEEE intelligent systemsArticle
Corrections and comments
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Authors and funding
8 authors at 5 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Since the variables inherent to various diseases cannot be controlled directly in humans, behavioral dysfunctions have been examined in model organisms, leading to better understanding their underlying mechanisms. However, because the spatial and temporal scales of animal locomotion vary widely among species, conventional statistical analyses cannot be used to discover knowledge from the locomotion data. We propose a procedure to automatically discover locomotion features shared among animal species by means of domain-adversarial deep neural networks. Our neural network is equipped with a function which explains the meaning of segments of locomotion where the cross-species features are hidden by incorporating an attention mechanism into the neural network, regarded as a black box. It enables us to formulate a human-interpretable rule about the cross-species locomotion feature and validate it using statistical tests. We demonstrate the versatility of this procedure by identifying locomotion features shared across different species with dopamine deficiency, namely humans, mice, and worms, despite their evolutionary differences.
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