Jove
Visualize
联系我们

相关概念视频

Proteomics01:33

Proteomics

7.9K
A proteome is the entire set of proteins that a cell type produces. We can study proteomes using the knowledge of genomes because genes code for mRNAs, and the mRNAs encode proteins. Although mRNA analysis is a step in the right direction, not all mRNAs are translated into proteins.
Proteomics is the study of proteomes' function. It involves the large-scale systematic study of the proteome to denote the protein complement expressed by a genome. Scientist Mark Wilkins coined the term...
7.9K

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

Theoretical design of optical fibers with protection of linear operation mode against external thermal and electric disturbances.

Scientific reports·2026
Same author

Advancements in psoriasis classification using custom transfer learning algorithms.

Scientific reports·2026
Same author

Enhancing Alzheimer's disease classification with a transformer-based model using self-supervised learning.

Scientific reports·2026
Same author

QBrainNet: harnessing enhanced quantum intelligence for advanced brain stroke prediction from medical imaging.

Frontiers in medicine·2025
Same author

A novel adaptive multi-scale wavelet Galerkin method for solving fuzzy hybrid differential equations.

Scientific reports·2025
Same author

A population based optimization of convolutional neural networks for chronic kidney disease prediction.

Scientific reports·2025
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

相关实验视频

Updated: Sep 14, 2025

An Organotypic High Throughput System for Characterization of Drug Sensitivity of Primary Multiple Myeloma Cells
09:41

An Organotypic High Throughput System for Characterization of Drug Sensitivity of Primary Multiple Myeloma Cells

Published on: July 15, 2015

8.7K

一个量子机器学习框架,用于预测多发性骨髓瘤中使用蛋白质学数据的药物敏感性.

M Priyadharshini1, B Deevena Raju2, A Faritha Banu3

  • 1Department of Computer Science & Engineering, Faculty of Science and Technology (IcfaiTech), ICFAI Foundation for Higher Education, Hyderabad, 501203, India.

Scientific reports
|July 22, 2025
PubMed
概括
此摘要是机器生成的。

这项研究介绍了QProteoML,一个量子机器学习框架,用于预测多发性髓瘤 (MM) 药物敏感性. 在识别药物耐药性方面,QProteoML优于经典模型,为个性化医学提供了洞察力.

关键词:
发现生物标志物的发现.药物敏感性预测 药物敏感性预测多发性骨髓瘤是一种多发性骨髓瘤.蛋白质组学数据在QSVM中使用QSVM.量子机器学习 (QML) 是一种

更多相关视频

Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

716
Quantitative Mass Spectrometric Profiling of Cancer-cell Proteomes Derived From Liquid and Solid Tumors
08:08

Quantitative Mass Spectrometric Profiling of Cancer-cell Proteomes Derived From Liquid and Solid Tumors

Published on: February 27, 2015

16.4K

相关实验视频

Last Updated: Sep 14, 2025

An Organotypic High Throughput System for Characterization of Drug Sensitivity of Primary Multiple Myeloma Cells
09:41

An Organotypic High Throughput System for Characterization of Drug Sensitivity of Primary Multiple Myeloma Cells

Published on: July 15, 2015

8.7K
Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

716
Quantitative Mass Spectrometric Profiling of Cancer-cell Proteomes Derived From Liquid and Solid Tumors
08:08

Quantitative Mass Spectrometric Profiling of Cancer-cell Proteomes Derived From Liquid and Solid Tumors

Published on: February 27, 2015

16.4K

科学领域:

  • 计算生物学 计算生物学
  • 量子计算是一种量子计算.
  • 在瘤学瘤学.

背景情况:

  • 多发性骨髓瘤 (MM) 是一种异质癌症,药物反应可变.
  • 经典的数据分析方法在MM蛋白质组分析中扎着高维度和不平衡的数据.
  • "维度的诅咒"导致MM的经典模型过拟合.

研究的目的:

  • 引入QProteoML,一个新的量子机器学习 (QML) 框架,用于预测MM的药物敏感性.
  • 为了应对高维度,数据不平衡和特征冗余在MM的蛋白质组数据分析中的挑战.
  • 提高MM中药物敏感性预测的准确性和通用性.

主要方法:

  • 量子支持向量机 (QSVM),量子主要组件分析 (qPCA),量子化 (QA) 和量子生成对抗网络 (QGAN) 的集成.
  • 利用诸如叠加和纠等量子现象进行非线性建模,缩小维度,处理类不平衡等.
  • QSVM用于复杂模式检测,qPCA用于维度减小期间的差异保存,QA用于生物标志物选择.

主要成果:

  • 与经典模型 (SVM,RF,LR,KNN) 相比,QProteoML在预测药物敏感性方面表现优越.
  • 该框架有效地确定了耐药性少数患者类别.
  • QProteoML提供了可解释的结果,突出了MM药物敏感性的关键生物标志物.

结论:

  • 通过准确的药物敏感性预测,QProteoML为多发性骨髓瘤的个性化医学提供了一个有前途的方法.
  • 量子算法显示了对复杂生物数据的可靠分析的潜力,提高了药物反应预测.
  • 未来的工作包括临床验证和量子硬件集成,用于MM中的实用QML应用.