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

2.2K
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. 
2.2K
Aggregates Classification01:29

Aggregates Classification

300
Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
300
Sensitivity, Specificity, and Predicted Value01:13

Sensitivity, Specificity, and Predicted Value

177
In healthcare diagnostics, laboratory tests play a crucial role in identifying and diagnosing a wide range of medical conditions. However, interpreting test results is not always straightforward. An abnormal test result does not always confirm the presence of a disease, just as a normal result does not guarantee its absence. To assess the reliability of these diagnostic tools, healthcare practitioners rely on two key statistical indicators: sensitivity and specificity.
Sensitivity is the...
177
Classification of Signals01:30

Classification of Signals

386
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
386
Expected Frequencies in Goodness-of-Fit Tests01:19

Expected Frequencies in Goodness-of-Fit Tests

2.5K
A goodness-of-fit test is conducted to determine whether the observed frequency values are statistically similar to the frequencies expected for the dataset. Suppose the expected frequencies for a dataset are equal such as when predicting the frequency of any number appearing when casting a die. In that case, the expected frequency is the ratio of the total number of observations (n)  to the number of categories (k).
2.5K
Predicting Products: Substitution vs. Elimination02:52

Predicting Products: Substitution vs. Elimination

11.4K
When a nucleophile and an alkyl halide react, nucleophilic substitution and β-elimination reactions compete to generate products.
The following factors can influence the mechanisms competing against each other:
11.4K

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

Bentracimab for Surgical and Perioperative Bleeding: Clinical Efficacy, Emerging Safety Concerns, and Implementation Challenges (Cost, Accessibility, and Evidence Gaps).

Cureus·2026
Same author

Clinical insights on impact of zone 2 preemptive TEVAR on treatment strategy in uncomplicated type B aortic dissection: a 10-year outcome, study of 29 cases with zone 2 placement.

General thoracic and cardiovascular surgery·2026
Same author

Multilevel analysis of salt stress responses in sorghum during seed germination.

Frontiers in plant science·2026
Same author

Interfacial ion diffusion and rapid charge transfer kinetics of the hydrothermally synthesized heterostructured Bi<sub>2</sub>WO<sub>6</sub>/Bi<sub>2</sub>O<sub>3</sub>/MXene composite for next-generation pseudocapacitors.

RSC advances·2026
Same author

Hepatitis C Virus Infection Induces Autoimmune Hypothyroidism with Potential Profound Metabolic Implications: A Cross-Sectional Study in a High-Prevalence Region.

Metabolites·2026
Same author

The GCP6-MPC5 module: a key genetic switch for balancing rice grain quality and environmental adaptability.

Plant cell reports·2026
Same journal

Mapping Evolution of Molecules across Biochemistry with Assembly Theory.

Journal of chemical information and modeling·2026
Same journal

Structural Proteomics-Based Deciphering of Hydrophobic Packing Fingerprints Informing Protein Thermostability in TIM Barrels.

Journal of chemical information and modeling·2026
Same journal

Bridging between Structure-Based and Data-Driven Affinity Prediction.

Journal of chemical information and modeling·2026
Same journal

Reinforcement Learning-Driven Multiproperty Optimization in Molecular Design Using Multicontext Transcriptome Data.

Journal of chemical information and modeling·2026
Same journal

EnsembleCycPerm: Interpretable Modeling of Cyclic Peptide Permeability through Solvent-Dependent Conformational Ensembles.

Journal of chemical information and modeling·2026
Same journal

Resolving Conformational Preferences of Monosaccharides from <sup>1</sup>H and <sup>13</sup>C NMR Chemical Shifts Using an Integrated MD and QM Approach.

Journal of chemical information and modeling·2026
查看所有相关文章

相关实验视频

Updated: Jun 2, 2025

Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

19.9K

多类合成可访问性预测

Xinqi Li1, Ryan Walsh2,3, Waseem Abbas1

  • 1X-Chem U.K., 1 Ashley Road, Altrincham, Cheshire WA14 2DT, U.K.

Journal of chemical information and modeling
|January 17, 2025
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种新的多类机器学习模型,用于预测化学合成难度. 这种新的方法通过处理数据不平衡和使用药物发现的灵活评估指标来提高准确性.

更多相关视频

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

632
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

491

相关实验视频

Last Updated: Jun 2, 2025

Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

19.9K
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

632
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

491

科学领域:

  • 计算化学是一种计算化学.
  • 药品化学 药品化学 是一个
  • 机器学习是机器学习.

背景情况:

  • 预测分子的合成可访问性在药物发现中至关重要.
  • 现有的二进制分类模型因数据不平衡和固定值而面临挑战.
  • 机器学习越来越多地用于预测合成的易度或难度.

研究的目的:

  • 开发一种新的多类分类方法,用于预测分子所需的最小合成步骤.
  • 在合成可访问性预测中解决二进制分类方法的局限性.
  • 引入模糊的评估指标,以实现更现实的绩效评估.

主要方法:

  • 开发了一种多类折叠组合分类方法.
  • 基础模型被训练在多层分层子样本折叠中,以减轻类不平衡.
  • 使用了概率或投票聚合策略.
  • 建议使用模糊评估指标来考虑预测公差.

主要成果:

  • 该模型在基准数据集上的多类合成可访问性预测中表现出有效性.
  • 拟议的方法在二元合成可访问性预测中表现优于现有的六种模型.
  • 折叠组装策略成功地缓解了阶级不平衡问题.

结论:

  • 新的多类方法提供了更细致和更准确的预测合成可访问性.
  • 模糊的评估指标提供了对模型性能更实用的评估.
  • 这项工作推动了机器学习在优化药物发现管道中的应用.