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

Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

5.6K
Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
5.6K

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

Self-Growing Bacterial Support for Synthesis of Metal Nanoparticles and Preparation of Sustainable M/Bact Biohybrid Catalysts. A Review.

ChemSusChem·2026
Same author

Deep learning-based high-information-content graph representation of early stage bacterial biofilms.

NPJ biofilms and microbiomes·2026
Same author

Cell Painting for cytotoxicity and mode-of-action analysis in primary human hepatocytes.

Cell systems·2026
Same author

Image-to-molecule benchmarking dataset with fractal pattern and hierarchical morphology recognition.

Scientific data·2026
Same author

Dual-Function Sol-Gel Antibacterial Materials with Biphasic Octenidine Release for Decontamination and Long-Term Protection.

ACS omega·2026
Same author

Pondering the Future of Chemical Research Amid the Wider Adoption of Artificial Intelligence Technologies.

Chemistry (Weinheim an der Bergstrasse, Germany)·2026

相关实验视频

Updated: May 21, 2025

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

1.1K

通过机器学习对分子复杂性的数字化.

Andrei S Tyrin1, Daniil A Boiko1, Nikita I Kolomoets1

  • 1Zelinsky Institute of Organic Chemistry, Russian Academy of Sciences Leninsky prospekt 47 Moscow 119991 Russia http://AnanikovLab.ru val@ioc.ac.ru.

Chemical science
|March 21, 2025
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种机器学习模型,以数量化量化分子复杂性,数字化化学和生命科学的人类感知. 这促进了结构-活性关系的发展和药物发现研究.

更多相关视频

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
09:34

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

Published on: September 25, 2021

3.9K
Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning
08:58

Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning

Published on: November 19, 2018

12.4K

相关实验视频

Last Updated: May 21, 2025

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

1.1K
A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
09:34

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

Published on: September 25, 2021

3.9K
Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning
08:58

Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning

Published on: November 19, 2018

12.4K

科学领域:

  • 计算化学是一种计算化学.
  • 化学信息学 化学信息学
  • 机器学习在药物发现中的作用

背景情况:

  • 量化分子复杂性对于理解结构-活性关系至关重要,但缺乏标准化.
  • 目前的措施依赖于直觉感知,阻碍客观分析和比较.
  • 这种差距限制了化学行为和生物活动研究的进步.

研究的目的:

  • 开发一种新的,标准化的分子复杂性的数值测量方法.
  • 利用机器学习来数字化人类对分子复杂性的专家感知.
  • 为分析化学和生物数据创建一个强大的框架.

主要方法:

  • 实施基于机器学习的框架,使用学习以排名 (LTR) 方法.
  • 在大约30万种不同的化学结构的大型数据集上训练的排名模型的开发.
  • 纳入人类专业知识,以捕捉分子复杂性评估中的直观决策规则.

主要成果:

  • 一个新的机器学习模型,能够从数值上量化分子复杂性.
  • 为了未来的研究,生成了各种化学结构的大型标记数据集.
  • 在分析有机化学趋势,FDA批准的药物和合成策略方面展示了应用.

结论:

  • 分子复杂性可以有效地用机器学习作为数值特征来量化.
  • 开发的框架为评估分子复杂性提供了一种标准化的方法.
  • 这种进步有助于更深入地了解结构-活性关系,并指导药物发现过程.