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

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...

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

3Mont: A multi-omics integrative tool for breast cancer subtype stratification.

PloS one·2025
Same author

Low Back Pain Among Health Sciences Undergraduates: Results Obtained from a Machine-Learning Analysis.

Journal of clinical medicine·2025
Same author

RCE-IFE: recursive cluster elimination with intra-cluster feature elimination.

PeerJ. Computer science·2025
Same author

The Role of MicroRNAs in HIV Infection.

Genes·2024
Same author

PriPath: identifying dysregulated pathways from differential gene expression via grouping, scoring, and modeling with an embedded feature selection approach.

BMC bioinformatics·2023
Same author

Community-wide collaboration is a must to reinstall trust in bioinformatics solutions and biomedical interpretation.

Journal of integrative bioinformatics·2023

相关实验视频

Updated: May 11, 2026

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

4.0K

生物信息学中的深度学习

Malik Yousef1, Jens Allmer2

  • 1Department of Information Systems, Zefat Academic College, Zefat, Israel.

Turkish journal of biology = Turk biyoloji dergisi
|April 29, 2024
PubMed
概括
此摘要是机器生成的。

本综述探讨了生物信息学中的深度学习应用,涵盖基因组测序,药物发现和疾病诊断. 它强调了研究人员使用这些强大的AI模型的技术和道德考虑.

关键词:
深度学习是一种深度学习.生物信息学是一种生物信息学.生物数据分析 生物数据分析神经网络的神经网络的神经网络

更多相关视频

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning
04:17

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

Published on: May 10, 2024

737
Author Spotlight: Advancing Alzheimer's Research – 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

1.0K

相关实验视频

Last Updated: May 11, 2026

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

4.0K
DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning
04:17

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

Published on: May 10, 2024

737
Author Spotlight: Advancing Alzheimer's Research – 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

1.0K

科学领域:

  • 生物信息学是一种生物信息学.
  • 机器学习 机器学习
  • 人工智能的人工智能

背景情况:

  • 深度学习,机器学习的一个子集,利用多层的人工神经网络来分析庞大的数据集.
  • 生物信息学整合了计算,数学和统计方法来分析生物数据.

研究的目的:

  • 审查生物信息学当前的深度学习应用.
  • 讨论生物数据分析中的深度学习的挑战和未来方向.
  • 为生物医学信息学研究人员指导深度学习模型实施的技术和伦理方面.

主要方法:

  • 综述生物信息学深度学习的最新进展.
  • 对各种生物领域的应用进行调查.
  • 讨论关键的技术和伦理方面的考虑.

主要成果:

  • 深度学习在诸如基因组测序,基因表达分析,蛋白质结构预测,药物发现和疾病诊断等领域显示出重大前景.
  • 确定的挑战包括数据要求,模型解释性和潜在的偏见.
  • 未来的机遇在于改进模型并解决道德问题.

结论:

  • 深度学习为生物信息学研究提供了强大的工具.
  • 收养需要仔细考虑公平,偏见,可解释性和问责制.
  • 本次审查鼓励在生物数据分析中更广泛,更负责任地使用深度学习.