期刊
PROGRESS IN NEURO-PSYCHOPHARMACOLOGY & BIOLOGICAL PSYCHIATRY
卷 112, 期 -, 页码 -出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.pnpbp.2021.110405
关键词
Neural network; Artificial intelligence; Locomotion; Zebrafish; CNS drug screening
资金
- Southwest University (Chongqing, China)
- St. Petersburg State University (SPSU) [51130521]
This study utilized AI neural network algorithms to analyze zebrafish locomotor track data, providing novel methods for neurophenotypic data collection and analysis. By training AI to recognize different psychotropic drugs and confirming its accuracy, a framework for improving movement pattern classification in zebrafish was presented, in order to advance drug discovery and development using this model organism.
Zebrafish (Danio rerio) are rapidly emerging in biomedicine as promising tools for disease modelling and drug discovery. The use of zebrafish for neuroscience research is also growing rapidly, necessitating novel reliable and unbiased methods of neurophenotypic data collection and analyses. Here, we applied the artificial intelligence (AI) neural network-based algorithms to a large dataset of adult zebrafish locomotor tracks collected previously in a series of in vivo experiments with multiple established psychotropic drugs. We first trained AI to recognize various drugs from a wide range of psychotropic agents tested, and then confirmed prediction accuracy of trained AI by comparing several agents with known similar behavioral and pharmacological profiles. Presenting a framework for innovative neurophenotyping, this proof-of-concept study aims to improve AI-driven movement pattern classification in zebrafish, thereby fostering drug discovery and development utilizing this key model organism.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据