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

Protein Networks02:26

Protein Networks

4.6K
An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
4.6K
Variability: Analysis01:11

Variability: Analysis

527
Measures of variability are statistical metrics that reveal the dispersion pattern within a dataset. They are pivotal in biostatistics, providing insights into the heterogeneity within health and biological data. Variability signifies the degree to which data points diverge from one another, helping researchers understand the potential range of values and associated uncertainty within the data.
The range is a simple measure of variability, indicating the difference between the highest and...
527
Random Variables01:09

Random Variables

17.9K
A random variable is a single numerical value that indicates the outcome of a procedure. The concept of random variables is fundamental to the probability theory and was introduced by a Russian mathematician, Pafnuty Chebyshev, in the mid-nineteenth century.
Uppercase letters such as X or Y denote a random variable. Lowercase letters like x or y denote the value of a random variable. If X is a random variable, then X is written in words, and x is given as a number.
For example, let X = the...
17.9K
Network Covalent Solids02:18

Network Covalent Solids

16.2K
Network covalent solids contain a three-dimensional network of covalently bonded atoms as found in the crystal structures of nonmetals like diamond, graphite, silicon, and some covalent compounds, such as silicon dioxide (sand) and silicon carbide (carborundum, the abrasive on sandpaper). Many minerals have networks of covalent bonds.
To break or to melt a covalent network solid, covalent bonds must be broken. Because covalent bonds are relatively strong, covalent network solids are typically...
16.2K
Graphs of Equations in Two Variables01:30

Graphs of Equations in Two Variables

270
An equation with two variables, typically written in the form y = f(x) or Ax + By = C, describes a relationship between quantities represented by x and y. Each solution to such an equation is an ordered pair (x, y) that satisfies the equation when substituted. These pairs can be represented graphically to understand the variables' relationship visually.A common technique for constructing the graph of a two-variable equation is to create a value table. Begin by choosing several values for the...
270
Variables Affecting Phosphorescence and Fluorescence01:26

Variables Affecting Phosphorescence and Fluorescence

1.5K
Fluorescence and phosphorescence are essential phenomena in fields like analytical chemistry, biological imaging, and materials science, where they detect molecular properties and visualize cellular structures. Understanding the variables that influence these luminescent behaviors is crucial for maximizing accuracy and efficiency in their applications. These variables can broadly be grouped into chemical structure, solvent properties, and external conditions, each playing a distinct role in...
1.5K

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

Heterogeneity-aware Clustered Distributed Learning for Multi-source Data Analysis.

Journal of machine learning research : JMLR·2026
Same author

Prognostic value of androgen receptor expression in ER-positive/HER2-negative breast cancer: evidence from a contemporary Chinese cohort.

Frontiers in oncology·2026
Same author

Medicare Insurance Type and Broad Genomic Profiling in Metastatic Cancer.

JAMA network open·2026
Same author

Doubly Robust Estimators of the Restricted Mean Time in Favor Estimands in Individual- and Cluster-Randomized Trials.

Statistics in medicine·2026
Same author

The Impact of Androgen Receptor Expression on Neoadjuvant Therapy in HER2-Positive Breast Cancer.

Breast cancer (Dove Medical Press)·2026
Same author

JOINT IDENTIFICATION OF SPATIALLY VARIABLE GENES VIA A NETWORK-ASSISTED BAYESIAN REGULARIZATION APPROACH.

The annals of applied statistics·2026

相关实验视频

Updated: Feb 13, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

1.1K

一个被惩罚的集成深度神经网络,用于在多个omics数据集中进行变量选择.

Yang Li1, Xiaonan Ren1, Haochen Yu1

  • 1Center for Applied Statistics School of Statistics Renmin University of China Beijing China.

Quantitative biology (Beijing, China)
|February 12, 2026
PubMed
概括
此摘要是机器生成的。

这项研究引入了一个惩罚性整合深度神经网络 (PIN) 用于OMIC数据分析. PIN 准确地从多个数据集中选择重要变量,即使样本大小小小,数据结构不同.

关键词:
深度学习是一种深度学习.综合性分析是一种综合性分析.有多个omics数据集.选择变量的选择变量.

更多相关视频

Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

10.0K
Author Spotlight: Exploring ShiDuGao's Multi-Target Approach in Anus Eczema Treatment
12:34

Author Spotlight: Exploring ShiDuGao's Multi-Target Approach in Anus Eczema Treatment

Published on: January 12, 2024

1.3K

相关实验视频

Last Updated: Feb 13, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

1.1K
Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

10.0K
Author Spotlight: Exploring ShiDuGao's Multi-Target Approach in Anus Eczema Treatment
12:34

Author Spotlight: Exploring ShiDuGao's Multi-Target Approach in Anus Eczema Treatment

Published on: January 12, 2024

1.3K

科学领域:

  • 生物信息学是一种生物信息学.
  • 计算生物学 计算生物学
  • 基因组学就是基因组学.

背景情况:

  • 深度学习模型越来越多地用于OMIC数据分析,可变选择提高了可解释性.
  • 现有的深度学习方法在omics数据中常见的小样本大小中扎,并且由于结构差异,在从多个研究中汇集数据时可能产生不准确的结果.

研究的目的:

  • 开发一种新的惩罚整合深度神经网络 (PIN),用于在多个omics数据集中同时进行变量选择.
  • 为了解决现有方法在处理小样本大小和跨数据集异质性的局限性,在整合性奥米克分析中.

主要方法:

  • 提出了一个处罚集成深度神经网络 (PIN),将多个数据集汇总为输入.
  • 在整合性框架内,PIN对不同数据集的均质和异质变量结构进行了计算.
  • 实现了同步变量选择,以识别各种omics数据中的重要特征.

主要成果:

  • 广泛的模拟和现实世界的应用表明PIN的性能优于现有方法.
  • 在多个数据集中,PIN实现了显著提高的变量选择精度,超过了纯粹的数据聚合.
  • 该方法成功地应用于来自对老年人认知状态和卵巢癌分期研究的基因表达数据集.

结论:

  • 拟议的PIN方法有效地从多个omics数据集中识别了与疾病相关的重要变量.
  • PIN 提供了一个强大的解决方案,用于整合性奥米克分析,处理小样本大小和跨数据集变化.
  • 免费可用的源代码 (rucliyang/PINFunc) 便于在未来的多个研究领域的研究中采用PIN.