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

The Sense of Self: Reflected Self-Appraisal and Social Comparison02:57

The Sense of Self: Reflected Self-Appraisal and Social Comparison

49.6K
According to Charles Cooley, we base our image on what we think other people see (Cooley 1902). We imagine how we must appear to others, then react to this speculation. We don certain clothes, prepare our hair in a particular manner, wear makeup, use cologne, and the like—all with the notion that our presentation of ourselves is going to affect how others perceive us. We expect a certain reaction, and, if lucky, we get the one we desire and feel good about it. But more than that, Cooley...
49.6K

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

Progress and new challenges in image-based profiling.

Molecular systems biology·2026
Same author

The Regulatory Interplay of the Colorectal Cancer Biomarkers MACC1 and IER2 and Its Impact on Metastatic Cancer Survival.

Biomolecules·2026
Same author

Cell Painting reveals polypharmacological activity on the V-ATPase by the F-ATPase inhibitor Cyhexatin.

Pesticide biochemistry and physiology·2026
Same author

Investigation of Radiolabeled KISS1R Ligands as Promising Tools for Diagnosis and Treatment of Triple-Negative Breast Cancer.

Molecular pharmaceutics·2026
Same author

A multimodal vision knowledge graph of cardiovascular disease.

Nature cardiovascular research·2025
Same author

Progress and new challenges in image-based profiling.

ArXiv·2025

相关实验视频

Updated: May 28, 2025

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning
12:06

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

Published on: March 3, 2023

3.9K

自我监督通过解锁强大的图像表示来推进形态分析.

Vladislav Kim1, Nikolaos Adaloglou2,3, Marc Osterland2

  • 1Machine Learning Research, Bayer AG, Berlin, Germany. vladislav.kim@bayer.com.

Scientific reports
|February 10, 2025
PubMed
概括

像DINO这样的自主监督学习 (SSL) 模型为传统的CellProfiler提供了一个计算效率高的替代方案. 这些人工智能模型提供了强大的表示,改善了药物标分类和对形态分析的概括性.

更多相关视频

Perceptual and Category Processing of the Uncanny Valley Hypothesis' Dimension of Human Likeness: Some Methodological Issues
07:34

Perceptual and Category Processing of the Uncanny Valley Hypothesis' Dimension of Human Likeness: Some Methodological Issues

Published on: June 3, 2013

17.3K
Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps
08:59

Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps

Published on: October 28, 2018

7.0K

相关实验视频

Last Updated: May 28, 2025

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning
12:06

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

Published on: March 3, 2023

3.9K
Perceptual and Category Processing of the Uncanny Valley Hypothesis' Dimension of Human Likeness: Some Methodological Issues
07:34

Perceptual and Category Processing of the Uncanny Valley Hypothesis' Dimension of Human Likeness: Some Methodological Issues

Published on: June 3, 2013

17.3K
Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps
08:59

Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps

Published on: October 28, 2018

7.0K

科学领域:

  • 计算生物学 计算生物学
  • 人工智能在药物发现中的作用
  • 基于图像的高内容选.

背景情况:

  • 细胞绘画是一种强大的基于图像的试验,用于了解药物机制和非目标效应.
  • 像CellProfiler这样的传统特征提取方法在计算上昂贵,需要大量的参数调整.
  • 需要更高效和自动化的方法来分析细胞绘画数据.

研究的目的:

  • 评估自主监督学习 (SSL) 模型对细胞绘画图像分析的有效性.
  • 将SSL衍生功能的性能与CellProfiler等传统方法进行比较.
  • 评估SSL特征的可复制性,生物相关性,预测能力和可转移性.

主要方法:

  • 在JUMP Cell Painting数据集的一个子集上训练SSL模型 (DINO,MAE,SimCLR).
  • 使用这些SSL模型提取图像表示.
  • 评估模型在任务上的表现,包括药物标分类,基因家族分类和生物活性预测.

主要成果:

  • 在药物标和基因家族分类方面,DINO SSL模型的表现优于CellProfiler,计算时间和成本大大降低.
  • 在没有微调的情况下,DINO显示出强大的概括性,在未见的基因扰乱数据集上表现优于CellProfiler.
  • 在生物活性预测方面,SSL模型的性能与监督方法相提并论,但差距很小.

结论:

  • 自主监督学习为使用细胞绘画图像进行形态分析提供了一种有效和高效的方法.
  • SSL模型,特别是DINO,提供了强大的,可泛化的表示,可以增强药物发现管道.
  • 这项研究强调了基于图像的生物数据的AI驱动分析的有希望的研究方向.