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

Forging New Pathways in Oncology: Strategic Insights from the 17th Annual Frontiers in Cancer Science Conference.

Cancer research·2026
Same author

(+)-Miliusol suppresses the Warburg effect and induces regulated cell death in triple-negative breast cancer through targeting EIF3D and remodeling cancer metabolism.

Acta pharmaceutica Sinica. B·2026
Same author

Deciphering Object Concepts: Hierarchical Cross-Modal Relational Reasoning for Mining Object-Attribute-Affordance Associations.

IEEE transactions on pattern analysis and machine intelligence·2026
Same author

Multi-region proteomic mapping identifies FTL1 and SERPINA3K as protective factors in cardiac aging.

Cell death & disease·2026
Same author

Exploring and Targeting the Connection of Iron and Copper Homeostasis to Neurodegenerative Diseases.

MedComm·2026
Same author

Integrating SAM Supervision for 3D Weakly Supervised Point Cloud Segmentation.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026

相关实验视频

Updated: Jul 5, 2025

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

758

基于深度学习的CETSA特征预测跨越多个细胞系与潜空间表示.

Shenghao Zhao1,2, Xulei Yang3, Zeng Zeng1

  • 1Institute for Infocomm Research (I2R), A*STAR, Singapore, 138632, Singapore.

Scientific reports
|January 22, 2024
PubMed
概括

本研究介绍了CycleDNN,这是一个深度学习框架,用于预测不同细胞系的细胞热转移试验 (CETSA) 特性. 这种计算方法减少了与MS-CETSA实验相关的时间和成本.

更多相关视频

P300-Based Brain-Computer Interface Speller Performance Estimation with Classifier-Based Latency Estimation
06:09

P300-Based Brain-Computer Interface Speller Performance Estimation with Classifier-Based Latency Estimation

Published on: September 8, 2023

598
Author Spotlight: Enhancing PSC-to-Functional Cell Differentiation Using ML Models Based on Live-Cell Bright-Field Imaging
11:38

Author Spotlight: Enhancing PSC-to-Functional Cell Differentiation Using ML Models Based on Live-Cell Bright-Field Imaging

Published on: October 4, 2024

577

相关实验视频

Last Updated: Jul 5, 2025

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

758
P300-Based Brain-Computer Interface Speller Performance Estimation with Classifier-Based Latency Estimation
06:09

P300-Based Brain-Computer Interface Speller Performance Estimation with Classifier-Based Latency Estimation

Published on: September 8, 2023

598
Author Spotlight: Enhancing PSC-to-Functional Cell Differentiation Using ML Models Based on Live-Cell Bright-Field Imaging
11:38

Author Spotlight: Enhancing PSC-to-Functional Cell Differentiation Using ML Models Based on Live-Cell Bright-Field Imaging

Published on: October 4, 2024

577

科学领域:

  • 生物物理学的生物物理.
  • 计算生物学 计算生物学
  • 蛋白质组学是指蛋白质组学.

背景情况:

  • 质谱结合细胞热转移测定 (MS-CETSA) 对于理解药物机制和蛋白质相互作用至关重要.
  • 在多个细胞系中执行MS-CETSA是资源密集的,限制了其广泛应用.

研究的目的:

  • 开发一个计算框架,CycleDNN,用于预测不同细胞系中的CETSA特征.
  • 通过实现跨细胞系的预测来克服实验MS-CETSA的局限性.

主要方法:

  • 循环DNN使用深度神经网络架构,具有多个自动编码器,用于循环特征预测.
  • 该模型使用预测损失,循环一致性损失和潜伏空间规范化进行训练.
  • 该框架通过共享的潜伏空间将CETSA特征转化为细胞系之间的特征.

主要成果:

  • 在公共数据集上的实验验证证证了CycleDNN方法的有效性.
  • 由CycleDNN生成的预测MS-CETSA数据通过蛋白质-蛋白质相互作用预测得到验证.
  • 这项研究证明了计算预测CETSA配置文件的可行性.

结论:

  • 循环DNN提供了一种计算效率高的方法,可以在各种细胞系中预测MS-CETSA特征.
  • 这一框架有可能显著降低MS-CETSA研究所需的成本和时间.
  • 预测的数据支持下游应用,如蛋白质-蛋白质相互作用分析.