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

Prediction Intervals01:03

Prediction Intervals

2.3K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
2.3K
Proteomics01:33

Proteomics

7.3K
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...
7.3K
End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

327
A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting...
327
Immunoprecipitation01:20

Immunoprecipitation

5.5K
Immunoprecipitation, or IP, is a widely used technique that employs protein-antibody interactions to isolate proteins or protein complexes in their native state for studying protein-protein interactions, quaternary structures, or supramolecular complexes. Various modifications of the technique, including chromatin IP, cross-linking IP, and fluorescence IP, are commonly used.
Chromatin Immunoprecipitation
Chromatin immunoprecipitation, also known as ChIP, is used to study protein-DNA or...
5.5K
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

513
This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
513
Cluster Sampling Method01:20

Cluster Sampling Method

11.9K
Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
11.9K

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

Interpretable spatial multi-omics data integration and dimensionality reduction with SpaMV.

Nature communications·2026
Same author

Construction of two drug-loaded gold nanoclusters@mesoporous polydopamine nanospheres and the synergistic treatment of abdominal aortic aneurysms.

Journal of materials chemistry. B·2026
Same author

NIR-II Neuromodulation Combined with Metabolite-Mediated Immunoregulation for Accelerated Deeply Located Nerve Repair.

ACS nano·2026
Same author

AWmeta Empowers Adaptively Weighted Transcriptomic Meta-Analysis.

Current issues in molecular biology·2026
Same author

Vanadium-Doped Bioactive Glass-Modified GelMA/CMCS/HA Injectable Hydrogel for Osteosarcoma Postoperative Therapy and Bone Regeneration.

Materials (Basel, Switzerland)·2026
Same author

T2T-Hub: a central platform for analyzing plant and animal telomere-to-telomere genomes.

Nucleic acids research·2026

相关实验视频

Updated: Jul 5, 2025

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.1K

揭示蛋白质冠状结构:通过重新采样嵌入和机器学习进行预测.

Rong Liao1, Yan Zhuang1, Xiangfeng Li1

  • 1College of Biomedical Engineering, National Engineering Research Centre for Biomaterials, Sichuan University, Chengdu, 610065, China.

Regenerative biomaterials
|January 12, 2024
PubMed
概括

预测纳米粒子蛋白冠状组成对于生物材料设计至关重要. 这项研究引入了重新采样嵌入,以提高机器学习模型准确度,用于蛋白质冠状病毒预测,增强生物材料开发.

关键词:
特性分析的特征分析.机器学习是机器学习.纳米颗粒是一种纳米粒子.蛋白质冠状病毒冠状病毒重新采样技术重新采样技术

更多相关视频

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
06:50

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

Published on: January 26, 2024

1.9K
Resolving Water, Proteins, and Lipids from In Vivo Confocal Raman Spectra of Stratum Corneum through a Chemometric Approach
09:32

Resolving Water, Proteins, and Lipids from In Vivo Confocal Raman Spectra of Stratum Corneum through a Chemometric Approach

Published on: September 26, 2019

7.2K

相关实验视频

Last Updated: Jul 5, 2025

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.1K
Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
06:50

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

Published on: January 26, 2024

1.9K
Resolving Water, Proteins, and Lipids from In Vivo Confocal Raman Spectra of Stratum Corneum through a Chemometric Approach
09:32

Resolving Water, Proteins, and Lipids from In Vivo Confocal Raman Spectra of Stratum Corneum through a Chemometric Approach

Published on: September 26, 2019

7.2K

科学领域:

  • 生物材料科学 生物材料科学
  • 纳米技术纳米技术
  • 计算生物学 计算生物学

背景情况:

  • 纳米粒子 (NP) 与生物流体相互作用,形成一个蛋白质冠状 (PC).
  • 准确的PC预测对于评估生物材料的骨诱导性和指导NP设计至关重要.
  • 现有的机器学习模型与不平衡的PC数据和极端值作斗争,限制了预测准确性.

研究的目的:

  • 开发一种改进的机器学习方法来预测蛋白质冠状组合.
  • 为了解决蛋白质冠状病毒预测模型中的数据不平衡问题.
  • 为了提高生物材料应用中预测纳米粒子-蛋白相互作用的准确性.

主要方法:

  • 引入重新采样嵌入技术来处理不平衡的蛋白质冠状病毒数据.
  • 评估各种机器学习模型,重点是随机森林 (RF) 模型.
  • 使用四种不同的NP (HA,TiO2,SiO2,Ag) 的无标签量化进行了废弃实验和验证.

主要成果:

  • 拟议的重新采样嵌入方法提高了预测准确度,达到0.68的R2 (约为0.68). 10%的改善) 和0.90的RMSE (大约. 减少了10%).
  • 随机过量抽样进一步提高了特定NP的预测性能,产生了R2值>0.70.
  • 特性分析确定了化期血度,PDI和表面修饰作为影响PC组成的关键因素.

结论:

  • 重新采样嵌入有效地解决了蛋白质冠状病毒预测中的数据不平衡问题.
  • 增强的射频模型提供了对蛋白质冠状结构的准确预测.
  • 这种方法有助于合理设计具有量身定制的生物相互作用的纳米材料.