Pham Tuan D
IEEE/ACM Trans Comput Biol Bioinform. 2023 Jan-Feb;20(1):206-216. doi: 10.1109/TCBB.2022.3153668. Epub 2023 Feb 3.
The free-living nematode Caenorhabditis elegans is an ideal model for understanding behavior and networks of neurons. Experimental and quantitative analyses of neural circuits and behavior have led to system-level understanding of behavioral genetics and process of transformation from sensory integration in stimulus environments to behavioral outcomes. The ability to differentiate locomotion behavior between wild-type and mutant Caenorhabditis elegans strains allows precise inference on and gaining insights into genetic and environmental influences on behaviors. This paper presents an eigenfeature-enhanced deep-learning method for classifying the dynamics of locomotion behavior of wild-type and mutant Caenorhabditis elegans. Classification results obtained from public benchmark time-series data of eigenworms illustrate the superior performance of the new method over several existing classifiers. The proposed method has potential as a useful artificial-intelligence tool for automated identification of the nematode worm behavioral patterns aiming at elucidating molecular and genetic mechanisms that control the nervous system.
自由生活的线虫秀丽隐杆线虫是理解行为和神经元网络的理想模型。对神经回路和行为进行实验和定量分析，已使人们从系统层面理解行为遗传学，以及从刺激环境中的感觉整合到行为结果的转变过程。区分野生型和突变型秀丽隐杆线虫菌株运动行为的能力，有助于精确推断并深入了解基因和环境对行为的影响。本文提出了一种特征增强的深度学习方法，用于对野生型和突变型秀丽隐杆线虫的运动行为动态进行分类。从特征虫的公共基准时间序列数据获得的分类结果表明，新方法比几种现有分类器具有更优的性能。所提出的方法有潜力成为一种有用的人工智能工具，用于自动识别线虫的行为模式，旨在阐明控制神经系统的分子和遗传机制。
