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

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

242
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
242
Cluster Sampling Method01:20

Cluster Sampling Method

14.0K
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...
14.0K

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

Deep kernel learning enhanced fusion for multimodal classification.

Neural networks : the official journal of the International Neural Network Society·2026
Same author

A novel modality contribution confidence-enhanced multimodal deep learning framework for multiomics data.

BMC bioinformatics·2025
Same author

Recent Advances in Food Drying Modeling: Empirical to Multiscale Physics-Informed Neural Networks.

Comprehensive reviews in food science and food safety·2025
Same author

Progress in CO<sub>2</sub> Gas Sensing Technologies: Insights into Metal Oxide Nanostructures and Resistance-Based Methods.

Micromachines·2025
Same author

Pre-gating and contextual attention gate - A new fusion method for multi-modal data tasks.

Neural networks : the official journal of the International Neural Network Society·2024
Same author

ZIF-8-Based Surface Plasmon Resonance and Fabry-Pérot Sensors for Volatile Organic Compounds.

Sensors (Basel, Switzerland)·2024
Same journal

MMFVS-Net: A triple-symmetric cross-attention network for multimodal optical image fusion and high-accuracy virtual staining of breast cancer tissues.

Computer methods and programs in biomedicine·2026
Same journal

A novel Milstein-stochastic epidemiologically-informed neural network for approaching epidemic dynamics: Application to Mpox disease.

Computer methods and programs in biomedicine·2026
Same journal

Accounting for approximation errors using surrogate-based parameter estimation of cardiac mechanics digital twins.

Computer methods and programs in biomedicine·2026
Same journal

Facial iPPG heatmap patterns based on period-aware autoencoder show association with carotid atherosclerosis towards non-contact hemodynamic assessment.

Computer methods and programs in biomedicine·2026
Same journal

Explainable machine learning models predict liver fibrosis risk and outcome in the general population: Development and multi-cohort external validation.

Computer methods and programs in biomedicine·2026
Same journal

Evaluation of surrogate endpoints for survival outcomes using the surrogate package in R.

Computer methods and programs in biomedicine·2026
查看所有相关文章

相关实验视频

Updated: Jan 15, 2026

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts
08:51

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

Published on: September 20, 2024

2.0K

半监督深度矩阵分解模型用于聚类多omics数据.

Khanh Luong1, Nirav Joshi1, Richi Nayak2

  • 1QUT Centre for Data Science, School of Computer Science, Queensland University of Technology, Brisbane, Queensland, Australia.

Computer methods and programs in biomedicine
|October 14, 2025
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种半监督深度非负矩阵因子化模型 (SSD-MO) 用于多omics数据集成. 通过有效利用标记和未标记样本,SSD-MO显著提高了集群精度和性能.

关键词:
深度矩阵分解因子化基因表达 基因表达 基因表达多个omics数据数据的数据.多视图数据多视图数据半监督的深度矩阵分解因子.

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

1.2K
Author Spotlight: Advancing Alzheimer's Research &#8211; 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

1.7K

相关实验视频

Last Updated: Jan 15, 2026

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts
08:51

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

Published on: September 20, 2024

2.0K
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

1.2K
Author Spotlight: Advancing Alzheimer's Research &#8211; 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

1.7K

科学领域:

  • 计算生物学 计算生物学
  • 生物信息学是一种生物信息学.
  • 数据科学数据科学数据科学

背景情况:

  • 多主题数据由于高维度,稀疏性和噪音而带来挑战.
  • 传统的方法在噪音,可解释性和捕捉非线性模式方面扎.
  • 现有的多视图非负矩阵分解方法在很大程度上没有监督.

研究的目的:

  • 开发一个强大的模型,用于多omics数据集成和集群.
  • 解决处理复杂,高维度生物数据的现有方法的局限性.
  • 为了提高性能,利用标记和未标记的样本.

主要方法:

  • 拟议的SSD-MO (半监督深度非负矩阵分解) 模型.
  • 结合了半监督学习与一个深层次的因素化框架.
  • 结合了保留几何结构,直角性和多样性的约束.

主要成果:

  • SSD-MO显著提高了6个多omics数据集的集群精度.
  • 与未经监督的基线相比,F-score提高了9%至24%.
  • 在精确度 (64%-73%) 和回忆 (70%-88%) 方面表现出强的性能.

结论:

  • SSD-MO为多omics数据集成提供了一个强大的框架.
  • 该方法对基因组学和精密医学的应用有希望.
  • 通过有效利用标记和未标记的数据来提高聚类性能.