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Deep phenotyping reveals movement phenotypes in mouse neurodevelopmental models.

Ugne KlibaiteMikhail KislinJessica L VerpeutSilke BergelerXiaoting SunJoshua W ShaevitzSamuel S-H Wang
Published in: Molecular autism (2022)
Our automated pipeline for deep phenotyping successfully captures model-specific deviations in adaptation and movement as well as differences in the detailed structure of behavioral dynamics. The reported deficits indicate that deep phenotyping constitutes a robust set of ASD symptoms that may be considered for implementation in clinical settings as quantitative diagnosis criteria.
Keyphrases
  • high throughput
  • autism spectrum disorder
  • primary care
  • healthcare
  • traumatic brain injury
  • machine learning
  • high resolution
  • deep learning
  • quality improvement