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

Improving Translational Accuracy02:07

Improving Translational Accuracy

Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
Predicting Reaction Outcomes02:24

Predicting Reaction Outcomes

Kinetics describes the rate and path by which a reaction occurs. In contrast, thermodynamics deals with state functions and describes the properties, behavior, and components of a system. It is not concerned with the path taken by the process and cannot address the rate at which a reaction occurs. Although it does provide information about what can happen during a reaction process, it does not describe the detailed steps of what appears on an atomic or a molecular level. On the other hand,...
Improving Translational Accuracy02:07

Improving Translational Accuracy

Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
Neural Circuits01:25

Neural Circuits

Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
Cognitive Learning01:21

Cognitive Learning

Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
E. C. Tolman's theory of purposive behavior emphasizes that much behavior is goal-directed. He argued that to understand behavior, we must look at the entire sequence of actions leading to a goal. For instance, high school students study hard, not just due to past reinforcement but also to achieve the goal of getting into a good college.
Tolman introduced the idea that behavior is influenced by...
Language Development01:22

Language Development

Children master language quickly and with relative ease, supported by both biological predisposition and reinforcement. B. F. Skinner (1957) proposed that language is learned through reinforcement, while Noam Chomsky (1965) argued that language acquisition mechanisms are biologically determined.
The critical period for language acquisition suggests that the ability to acquire language is at its peak early in life. As people age, this proficiency decreases. Language development begins very...

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

Meta-analysis of the clinical efficacy of microcatheter-assisted trabeculotomy in the treatment of glaucoma.

Journal of investigative medicine : the official publication of the American Federation for Clinical Research·2026
Same author

Interpretable modality-aware mapping of gene regulation in single-cell multiomics with scMAGCA.

Nature communications·2026
Same author

Bridging sequence-structure motifs and genetic variants for genome-wide dynamic RNA-protein interaction profiling.

Nature communications·2026
Same author

Olfactory Decline in Elderly at High Altitudes: A Narrative Review.

International journal of general medicine·2026
Same author

Development and Interpretation of a Machine Learning-Based Predictive Model Using Clinical Parameters for Eosinophilic Chronic Rhinosinusitis With Nasal Polyps.

American journal of rhinology & allergy·2026
Same author

Orthogonal disentanglement of single-cell multi-omics reveals private and shared drivers of tissue development and pathogenesis.

Proceedings of the National Academy of Sciences of the United States of America·2026

相关实验视频

Updated: May 8, 2026

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues
10:12

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

18.7K

scOTM:使用大型语言模型预测单细胞扰乱反应的深度学习框架

Yuchen Wang1, Tianchi Lu1, Xingjian Chen2

  • 1Department of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong SAR 999077, China.

Bioengineering (Basel, Switzerland)
|August 28, 2025
PubMed
概括

我们开发了scOTM, 一种深度学习模型, 这种方法克服了现有方法的局限性,通过灵活地建模转录转移并将其推广到新的细胞类型.

关键词:
深度学习大型语言模型最好的运输单细胞扰动预测

更多相关视频

Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq
06:24

Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq

Published on: March 12, 2021

3.7K
Author Spotlight: Enhancing PSC-to-Functional Cell Differentiation Using ML Models Based on Live-Cell Bright-Field Imaging
11:38

Author Spotlight: Enhancing PSC-to-Functional Cell Differentiation Using ML Models Based on Live-Cell Bright-Field Imaging

Published on: October 4, 2024

684

相关实验视频

Last Updated: May 8, 2026

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues
10:12

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

18.7K
Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq
06:24

Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq

Published on: March 12, 2021

3.7K
Author Spotlight: Enhancing PSC-to-Functional Cell Differentiation Using ML Models Based on Live-Cell Bright-Field Imaging
11:38

Author Spotlight: Enhancing PSC-to-Functional Cell Differentiation Using ML Models Based on Live-Cell Bright-Field Imaging

Published on: October 4, 2024

684

科学领域:

  • 计算生物学
  • 基因组学
  • 机器学习

背景情况:

  • 对单细胞药物反应的准确建模对于医疗保健至关重要.
  • 目前的方法与未配对的数据扎, 缺乏生物解释性.
  • 现有的模型往往需要限制性预先对齐,从而限制它们的表达力.

研究的目的:

  • 开发一个深度学习框架,scOTM,用于从未配对的数据中预测单细胞扰动反应.
  • 提高对未见细胞类型的概括性,并提高生物解释性.
  • 克服现有方法在处理未配对数据和严格的先前约束方面的局限性.

主要方法:

  • scOTM将来自大型语言模型的生物知识集成到一个变化自编码器中.
  • 最大平均差异规范化允许转录转移的灵活建模.
  • 最佳运输可以在控制和扰乱细胞分布之间建立可解释的映射.

主要成果:

  • 在预测全转录组反应和识别差异表达基因方面,scOTM 的性能优于现有的方法.
  • 在数据有限的场景中,该框架表现出卓越的稳定性.
  • scOTM 在多种细胞类型中表现出强大的泛化能力.

结论:

  • scOTM为预测单细胞药物反应提供了强大而灵活的框架.
  • 该方法通过提供可解释的嵌入和灵活的建模来增强生物学理解.
  • scOTM通过有效处理未配对的数据和对新细胞类型的概括来推进该领域.