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

Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

35
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
35

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

scDeepAPA: a deep learning framework for single-cell alternative polyadenylation identification.

Briefings in bioinformatics·2026
Same author

VIRSE: a variational Bayesian framework for RNA structural ensemble inference.

Briefings in bioinformatics·2026
Same author

Socioeconomic and Clinical Determinants Driving Access to BRCA Genetic Testing in Cancer : A Case-Control Study Using Observational Electronic Health Records Across Multiple Sites.

medRxiv : the preprint server for health sciences·2026
Same author

SpaGene: A Deep Adversarial Framework for Spatial Gene Imputation.

Computational and structural biotechnology journal·2026
Same author

ShapeRNA: an integrated web server for RNA secondary structure, ensemble, and functional analysis.

Nucleic acids research·2026
Same author

GatorST: A Versatile Contrastive Meta-Learning Framework for Spatial Transcriptomic Data Analysis.

Small methods·2026
Same journal

STED: flexible cross-modal topic modeling infers cell-type-specific regulatory landscapes from bulk epigenomics.

Briefings in bioinformatics·2026
Same journal

A knowledge-guided deep learning framework for quantitative nucleic acid testing.

Briefings in bioinformatics·2026
Same journal

Optimal transport for label transfer in single-cell multi-omics integration.

Briefings in bioinformatics·2026
Same journal

Continuous multi-omics pathway enrichment analysis resolves hidden functional heterogeneity.

Briefings in bioinformatics·2026
Same journal

Evaluating completeness, coherence, and consistency of genome-scale function annotations.

Briefings in bioinformatics·2026
Same journal

Transformers for single-cell RNA sequencing: a survey.

Briefings in bioinformatics·2026
查看所有相关文章

相关实验视频

Updated: Jun 17, 2025

Monitoring Spatial Segregation in Surface Colonizing Microbial Populations
07:40

Monitoring Spatial Segregation in Surface Colonizing Microbial Populations

Published on: October 29, 2016

11.0K

xSiGra:一个单细胞空间数据阐释的可解释模型.

Aishwarya Budhkar1, Ziyang Tang2, Xiang Liu3

  • 1Luddy School of Informatics, Computing, and Engineering, Indiana University Bloomington, 107 S Indiana Ave, Bloomington, IN 47405, United States.

Briefings in bioinformatics
|August 9, 2024
PubMed
概括
此摘要是机器生成的。

一个新的AI模型xSiGra使用多式成像数据解读空间细胞类型. 它揭示了关键的基因和细胞相互作用,揭示了细胞活动受到其邻居的影响.

关键词:
可以解释的人工智能AI混合图形变压器 混合图形变压器可以解释的特征.空间细胞识别功能

更多相关视频

Quantifying Spatiotemporal Parameters of Cellular Exocytosis in Micropatterned Cells
10:21

Quantifying Spatiotemporal Parameters of Cellular Exocytosis in Micropatterned Cells

Published on: September 16, 2020

6.1K
Mapping the Emergent Spatial Organization of Mammalian Cells using Micropatterns and Quantitative Imaging
09:56

Mapping the Emergent Spatial Organization of Mammalian Cells using Micropatterns and Quantitative Imaging

Published on: April 30, 2019

6.5K

相关实验视频

Last Updated: Jun 17, 2025

Monitoring Spatial Segregation in Surface Colonizing Microbial Populations
07:40

Monitoring Spatial Segregation in Surface Colonizing Microbial Populations

Published on: October 29, 2016

11.0K
Quantifying Spatiotemporal Parameters of Cellular Exocytosis in Micropatterned Cells
10:21

Quantifying Spatiotemporal Parameters of Cellular Exocytosis in Micropatterned Cells

Published on: September 16, 2020

6.1K
Mapping the Emergent Spatial Organization of Mammalian Cells using Micropatterns and Quantitative Imaging
09:56

Mapping the Emergent Spatial Organization of Mammalian Cells using Micropatterns and Quantitative Imaging

Published on: April 30, 2019

6.5K

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.
  • 人工智能在医学中的应用

背景情况:

  • 空间成像技术提供高分辨率的单细胞数据,包括基因表达和空间位置.
  • 了解复杂的细胞相互作用和识别不同的细胞类型对于生物学见解至关重要.

研究的目的:

  • 介绍xSiGra,一个可解释的基于图形的AI模型,用于阐明空间细胞类型及其特征.
  • 利用空间成像的多式联络数据来增强生物发现.

主要方法:

  • 使用免疫组织学图像和基因表达数据构建空间细胞图.
  • 采用混合图形变压器模型进行空间细胞类型划分.
  • 整合一个梯度加权类激活映射变体用于可解释的特征识别.

主要成果:

  • 与现有方法相比,xSiGra在各种空间成像数据集中表现出卓越的性能.
  • 对肺瘤切片的分析揭示了细胞重要性得分,突出显示了邻近细胞对细胞活动的影响.
  • 鉴定出可解释的基因揭示了内皮细胞和瘤细胞之间的相互作用,这表明了复杂的机制.

结论:

  • xSiGra提供了一个强大的工具,用于分析复杂的空间奥米克数据.
  • 该模型通过识别关键基因和细胞与细胞相互作用来促进更深入的生物学见解.
  • 这种方法提高了我们对细胞异质性和微环境动态的理解.