Automatic quantification of disgust reactions in mice using machine learning
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| Publicado en: | bioRxiv (Jan 28, 2025) |
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| Autor principal: | |
| Otros Autores: | , |
| Publicado: |
Cold Spring Harbor Laboratory Press
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| Acceso en línea: | Citation/Abstract Full text outside of ProQuest |
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| 001 | 3160657502 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2692-8205 | ||
| 024 | 7 | |a 10.1101/2023.04.23.538002 |2 doi | |
| 035 | |a 3160657502 | ||
| 045 | 0 | |b d20250128 | |
| 100 | 1 | |a Inaba, Shizuki | |
| 245 | 1 | |a Automatic quantification of disgust reactions in mice using machine learning | |
| 260 | |b Cold Spring Harbor Laboratory Press |c Jan 28, 2025 | ||
| 513 | |a Working Paper | ||
| 520 | 3 | |a Disgust is a primary negative emotion that is crucial for avoiding intoxication and infection. Disgust in rodents has been quantified as the score of disgust reactions triggered by an unpleasant taste. Disgust reactions were video-recorded and manually quantified, requiring significant time and effort for analysis. Here we developed a method to automatically count disgust reactions in mice by using machine learning. The disgust reactions were automatically tracked using DeepLabCut as the coordinates of the nose and both front and rear paws. The automated tracking data were split into test and training data, and the latter were combined with manually labeled data on whether a disgust reaction was present and, if so, which type of disgust reaction was present. Then, a random forest classifier was constructed, and the performance of the classifier was evaluated in the test dataset. The total number of disgust reactions estimated by the classifier highly correlated with those counted manually (Pearson's r = 0.98). The present method will decrease the time and effort required to analyze disgust reactions, thus facilitating the implementation of the taste reactivity test in large-scale screening and long-term experiments that necessitate quantifying a substantial number of disgust reactions.Competing Interest StatementThe authors have declared no competing interest.Footnotes* Added details on methods and results. | |
| 653 | |a Emotions | ||
| 653 | |a Machine learning | ||
| 653 | |a Intoxication | ||
| 653 | |a Learning algorithms | ||
| 653 | |a Taste aversion learning | ||
| 700 | 1 | |a Uesaka, Naofumi | |
| 700 | 1 | |a Tanaka, Daisuke H | |
| 773 | 0 | |t bioRxiv |g (Jan 28, 2025) | |
| 786 | 0 | |d ProQuest |t Biological Science Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3160657502/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full text outside of ProQuest |u https://www.biorxiv.org/content/10.1101/2023.04.23.538002v2 |