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

Time-Series Graph00:54

Time-Series Graph

5.0K
A time-series graph is a line graph with repeated measurements taken at successive intervals of time. It is also called a time series chart. To construct a time-series graph, one must look at both pieces of a paired data set. The horizontal axis is used to plot the time increments, and the vertical axis is used to plot the values of the variable that one is measuring. By using the axes in this way, each point on the graph will correspond to time and a measured quantity. The points on the graph...
5.0K
Drug Concentration Versus Time Correlation01:15

Drug Concentration Versus Time Correlation

2.0K
The plasma drug concentration-time curve is a crucial tool in pharmacokinetics, representing the drug's concentration in plasma at different time intervals post-administration. This curve illustrates the drug's journey from absorption into the systemic circulation, distribution to body tissues, and eventual elimination through excretion or biotransformation.
Two pivotal parameters are the minimum effective concentration (MEC) and the minimum toxic concentration (MTC). The MEC is the...
2.0K

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

The peptide PROTAC modality: a novel strategy for targeted protein ubiquitination.

Theranostics·2020
Same author

PGC-1α and ERRα in patients with endometrial cancer: a translational study for predicting myometrial invasion.

Aging·2020
Same author

Beneficial phytoestrogenic effects of resveratrol on polycystic ovary syndromein rat model.

Gynecological endocrinology : the official journal of the International Society of Gynecological Endocrinology·2020
Same author

Long non-coding RNA MEG3 promotes cataractogenesis by upregulating TP53INP1 expression in age-related cataract.

Experimental eye research·2020
Same author

Chronic Resistance Exercise Improves Functioning and Reduces Toll-Like Receptor Signaling in Elderly Patients With Postoperative Deconditioning.

Journal of manipulative and physiological therapeutics·2020
Same author

Combating COVID-19 with integrated traditional Chinese and Western medicine in China.

Acta pharmaceutica Sinica. B·2020

相关实验视频

Updated: Jan 15, 2026

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps
11:52

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps

Published on: February 9, 2017

6.5K

针对时间间隔相关模式的有针对性的挖掘.

Shuang Liang, Lili Chen, Wensheng Gan

    IEEE transactions on neural networks and learning systems
    |October 14, 2025
    PubMed
    概括

    这项研究介绍了TaTIRP,这是一个针对有针对性的时间间隔相关模式 (TIRP) 挖矿的新算法. 它有效地发现了考虑到事件持续时间的模式,改善了医疗保健和金融等应用程序的数据分析.

    科学领域:

    • 数据挖掘 数据挖掘
    • 模式识别 模式识别
    • 计算科学 计算科学

    背景情况:

    • 序列模式挖掘通常忽视事件持续时间,将时间事件视为单个点.
    • 时间间隔相关模式 (TIRP) 挖矿通过考虑事件持续时间来解决这个问题,在医疗保健和金融领域有应用.
    • 挖掘所有可能的TIRP是计算密集型和资源密集型.

    研究的目的:

    • 提出一个新的算法,TaTIRP,用于发现有针对性的时间间隔相关模式 (TIRPs).
    • 通过专注于特定标准,提高TIRP采矿的效率和准确性.
    • 改进对需要考虑事件持续时间的应用程序的数据分析.

    主要方法:

    • 为有针对性的 TIRP 发现开发 TaTIRP 算法.
    • 实施多个修剪策略以消除冗余计算.
    • 对各种现实世界和合成数据集的评估.

    主要成果:

    • TaTIRP证明了有效地发现了有针对性的TIRP.
    • 修剪策略显著提高大规模数据集的性能.
    • 实验结果验证了算法的准确性和效率.

    更多相关视频

    Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons
    07:59

    Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons

    Published on: June 9, 2023

    1.9K
    Mass Spectrometry-Guided Genome Mining as a Tool to Uncover Novel Natural Products
    11:13

    Mass Spectrometry-Guided Genome Mining as a Tool to Uncover Novel Natural Products

    Published on: March 12, 2020

    11.6K

    相关实验视频

    Last Updated: Jan 15, 2026

    Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps
    11:52

    Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps

    Published on: February 9, 2017

    6.5K
    Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons
    07:59

    Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons

    Published on: June 9, 2023

    1.9K
    Mass Spectrometry-Guided Genome Mining as a Tool to Uncover Novel Natural Products
    11:13

    Mass Spectrometry-Guided Genome Mining as a Tool to Uncover Novel Natural Products

    Published on: March 12, 2020

    11.6K

    结论:

    • TaTIRP提供了一种有效的方法来挖掘与时间间隔相关的模式.
    • 有针对性的挖矿可以提高数据分析的效率和相关性.
    • 该算法为时间数据分析提供了有价值的工具,特别是在医疗保健和金融等领域.