Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Mutant superoxide dismutase 1-catalyzed hydrogen therapy for amyotrophic lateral sclerosis achieved by intercepting oxidative stress-neuroinflammation crosstalk.

Acta biomaterialia·2026
Same author

Recombinant human growth hormone in pediatric patients using the FAERS database: Safety profile and update for the period 2004 - 2024.

International journal of clinical pharmacology and therapeutics·2026
Same author

scRADAR: Dissecting intratumoral drug response heterogeneity at single-cell resolution via mechanism-guided prototype routing.

PLoS computational biology·2026
Same author

Farnesoid X receptor and nuclear factor erythroid 2-related factor 2 synergistically ameliorate cholestatic liver injury by coordinating bile acid metabolism and transport.

Chemico-biological interactions·2026
Same author

Correction: Carrier-free supramolecular nanoassemblies of pure LSD1 inhibitor for effective anti-tumor therapy.

Frontiers in chemistry·2026
Same author

From "negative" trial to positive clinical impact: mitigating eligibility criteria-induced temporal selection bias in emulated clinical trials.

npj health systems·2026
Same journal

Another 10 years of PLOS Computational Biology: A data-driven reflection on trends in genomics research.

PLoS computational biology·2026
Same journal

Mobility data resolution needed to inform predictive models of spatial epidemic spread from mobile phone data.

PLoS computational biology·2026
Same journal

DeepMethylation: A deep learning framework for tissue-specific DNA methylation prediction and functional variant annotation.

PLoS computational biology·2026
Same journal

Redefining and estimating the early-phase reproduction ratio for epidemic outbreaks in spatially structured populations.

PLoS computational biology·2026
Same journal

Optimized phenotype definitions boost GWAS power.

PLoS computational biology·2026
Same journal

Detection, communication, and individual identification with deep audio embeddings: A case study with North Atlantic right whales.

PLoS computational biology·2026
查看所有相关文章

相关实验视频

Updated: Jan 11, 2026

C. elegans Tracking and Behavioral Measurement
07:36

C. elegans Tracking and Behavioral Measurement

Published on: November 17, 2012

19.7K

通过基于深度学习的检测和跟踪进行自动化的C. elegans行为分析.

Xiaoke Liu1,2, Jianming Liu1,2, Wenjie Teng1

  • 1School of Basic Medical Sciences, Shandong Second Medical University, Weifang, Shandong, China.

PLoS computational biology
|November 11, 2025
PubMed
概括
此摘要是机器生成的。

我们开发了一个自动化的深度学习系统,用于跟踪Caenorhabditis elegans (C. elegans) 的行为. 这种高通量方法精确地同时分析多个虫,提高了遗传学和药物查研究的效率.

更多相关视频

Automated Behavioral Analysis of Large C. elegans Populations Using a Wide Field-of-view Tracking Platform
07:20

Automated Behavioral Analysis of Large C. elegans Populations Using a Wide Field-of-view Tracking Platform

Published on: November 28, 2018

9.7K
Automated Analysis of C. elegans Fluorescence Images using SegElegans
06:27

Automated Analysis of C. elegans Fluorescence Images using SegElegans

Published on: October 10, 2025

550

相关实验视频

Last Updated: Jan 11, 2026

C. elegans Tracking and Behavioral Measurement
07:36

C. elegans Tracking and Behavioral Measurement

Published on: November 17, 2012

19.7K
Automated Behavioral Analysis of Large C. elegans Populations Using a Wide Field-of-view Tracking Platform
07:20

Automated Behavioral Analysis of Large C. elegans Populations Using a Wide Field-of-view Tracking Platform

Published on: November 28, 2018

9.7K
Automated Analysis of C. elegans Fluorescence Images using SegElegans
06:27

Automated Analysis of C. elegans Fluorescence Images using SegElegans

Published on: October 10, 2025

550

科学领域:

  • 神经科学和行为生物学.
  • 计算生物学和生物信息学
  • 遗传学和分子生物学 遗传学和分子生物学

背景情况:

  • 手动跟踪Caenorhabditis elegans (C. elegans) 运动是低效和劳动密集型的.
  • 自动化分析对于对C. elegans行为进行高通量研究至关重要.
  • 现有的方法缺乏精度和效率,无法同时分析多个虫.

研究的目的:

  • 开发一个使用深度学习进行精确的C. elegans行为分析的自动化,高通量框架.
  • 为了提高C. elegans的检测和跟踪的准确性和连续性.
  • 建立一个强大的方法,对复杂的虫行为进行定量分析.

主要方法:

  • 实现了一个增强的虫检测框架,将YOLOv8与ByteTrack集成,实现实时多虫跟踪.
  • 开发了一种用于定量分析运动参数 (速度,车身曲,滚动频率) 的高通量自动化方法.
  • 利用深度学习进行精确的C. elegans检测,跟踪和行为参数提取.

主要成果:

  • 实现了高精度 (99.5%),回忆 (98.7%) 和mAP50 (99.6%) 的处理速度为153 FPS.
  • 与现有方法相比,证明了卓越的检测和跟踪准确性,连续性和稳定性.
  • 启用了高精度的多个C. elegans的同时跟踪和自动行为分析.

结论:

  • 开发的框架在自动化C. elegans行为分析方面取得了重大进展.
  • 这种高通量方法提高了C. elegans研究的实验效率和标准化.
  • 该系统为药物查,基因功能研究和理解复杂行为提供了宝贵的工具.