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

<i>Timmia sanjiangyuanensis</i> (Timmiaceae, Bryophyta), a new species from the Qinghai Plateau, China.

PhytoKeys·2026
Same author

Dezocine-mediated reversal of sufentanil-induced respiratory depression and its mitigation of adverse reactions: A clinical investigation.

Drug and alcohol dependence·2026
Same author

Corrigendum to "Tongfu-Xingshen capsule ameliorates ischemic stroke by inhibiting pyroptosis-mediated neuroinflammation" [J. Ethnopharmacol. 366 (2026) 121565].

Journal of ethnopharmacology·2026
Same author

Unveiling the improvement mechanisms of moist-heat and hot-air treatments on the flavor of Panax quinquefolius.

NPJ science of food·2026
Same author

Momordin Ic targets SREBP1 to disrupt lipid homeostasis and trigger synergistic apoptosis-ferroptosis in triple-negative breast cancer.

Phytomedicine : international journal of phytotherapy and phytopharmacology·2026
Same author

Salinity-induced microbial acclimation and kinetic responses in continuous self-circulating granular sludge process.

Bioresource technology·2026

相关实验视频

Updated: May 27, 2025

Simultaneous Affinity Enrichment of Two Post-Translational Modifications for Quantification and Site Localization
12:11

Simultaneous Affinity Enrichment of Two Post-Translational Modifications for Quantification and Site Localization

Published on: February 27, 2020

6.8K

ROASMI:通过重新利用保留数据来加速小分子识别.

Fang-Yuan Sun1, Ying-Hao Yin1,2, Hui-Jun Liu1

  • 1State Key Laboratory of Natural Medicines, Department of Chinese Medicines Analysis, School of Traditional Chinese Pharmacy, China Pharmaceutical University, No. 24 Tongjia Lane, Nanjing, 210009, China.

Journal of cheminformatics
|February 14, 2025
PubMed
概括
此摘要是机器生成的。

通过可靠地预测保留顺序,ROASMI模型增强了在非目标代谢学中的小分子识别. 这种方法提高了数据的可复制性,并有助于区分异构体和注释未知的化合物.

关键词:
深度学习是一种深度学习.代谢学 代谢学 代谢学可复制性 可复制性扣留命令是指一个保留命令.小分子识别小分子识别

更多相关视频

Large Scale Non-targeted Metabolomic Profiling of Serum by Ultra Performance Liquid Chromatography-Mass Spectrometry UPLC-MS
07:34

Large Scale Non-targeted Metabolomic Profiling of Serum by Ultra Performance Liquid Chromatography-Mass Spectrometry UPLC-MS

Published on: March 14, 2013

12.7K
Identifying Per- and Polyfluorinated Chemical Species with a Combined Targeted and Non-Targeted-Screening High-Resolution Mass Spectrometry Workflow
09:04

Identifying Per- and Polyfluorinated Chemical Species with a Combined Targeted and Non-Targeted-Screening High-Resolution Mass Spectrometry Workflow

Published on: April 18, 2019

12.3K

相关实验视频

Last Updated: May 27, 2025

Simultaneous Affinity Enrichment of Two Post-Translational Modifications for Quantification and Site Localization
12:11

Simultaneous Affinity Enrichment of Two Post-Translational Modifications for Quantification and Site Localization

Published on: February 27, 2020

6.8K
Large Scale Non-targeted Metabolomic Profiling of Serum by Ultra Performance Liquid Chromatography-Mass Spectrometry UPLC-MS
07:34

Large Scale Non-targeted Metabolomic Profiling of Serum by Ultra Performance Liquid Chromatography-Mass Spectrometry UPLC-MS

Published on: March 14, 2013

12.7K
Identifying Per- and Polyfluorinated Chemical Species with a Combined Targeted and Non-Targeted-Screening High-Resolution Mass Spectrometry Workflow
09:04

Identifying Per- and Polyfluorinated Chemical Species with a Combined Targeted and Non-Targeted-Screening High-Resolution Mass Spectrometry Workflow

Published on: April 18, 2019

12.3K

科学领域:

  • 分析化学 分析化学
  • 代谢学 代谢学 代谢学
  • 计算化学的计算化学

背景情况:

  • 在非目标代谢学中,保留数据的有限可复制性阻碍了小分子的识别.
  • 现有的保留订单模型缺乏通用性和预测可靠性.

研究的目的:

  • 介绍ROASMI模型,以可靠地预测逆相液态染色学 (RPLC) 中的保留顺序.
  • 通过增强数据可重复性来改善非目标代谢学中的小分子识别.

主要方法:

  • 将数据驱动的分子表示与机械洞察结合起来,开发 ROASMI 模型.
  • 在71个独立的RPLC数据集中验证ROASMI的概括性.
  • 将ROASMI应用于现实世界的数据集,用于异构体差异化和峰值注释.

主要成果:

  • 罗阿斯米在各种RPLC数据集中证明了可通用性.
  • 该模型有效地区分了具有相似碎片化模式的共存异构体.
  • ROASMI有助于注释缺乏信息频谱的检测峰值.

结论:

  • 罗阿斯米能够可靠地预测保留顺序,解决了代谢学中的关键局限性.
  • 该模型的灵活性允许重新训练和与其他MS/MS得分器相兼容,以改进小分子识别.