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

5.7K
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...
5.7K
¹³C NMR: Distortionless Enhancement by Polarization Transfer (DEPT)01:20

¹³C NMR: Distortionless Enhancement by Polarization Transfer (DEPT)

1.0K
When proton-coupled carbon-13 spectra are simplified by a broadband proton decoupling technique, structural information about the coupled protons is lost. Distortionless enhancement by polarization transfer (DEPT) is a technique that provides information on the number of hydrogens attached to each carbon in a molecule. While the DEPT experiment utilizes complex pulse sequences, the pulse delay and flip angle are specifically manipulated. The resulting signals have different phases depending on...
1.0K

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

Simplifying Cohort Definition with a Conversational Query Builder.

Studies in health technology and informatics·2026
Same author

A Privacy Index Calculator for Federated Medical Studies.

Studies in health technology and informatics·2026
Same author

Biochef: a client-side WebAssembly-based workflow builder for genomic data analysis.

BMC bioinformatics·2026
Same author

A systematic review and benchmarking of modern metagenomic tools for taxonomic classification.

Computers in biology and medicine·2026
Same author

HYMET: a hybrid metagenomic pipeline for accurate and efficient taxonomic classification.

GigaScience·2026
Same author

A Semantic-Driven for Cohort Data Harmonisation into OMOP CDM Schema.

Studies in health technology and informatics·2025
Same journal

Real-time EEG-based epileptic seizure prediction using artificial intelligence: A systematic review.

Artificial intelligence in medicine·2026
Same journal

R-peak detection and ECG data compression scheme based on empirical mode decomposition and wavelet transform.

Artificial intelligence in medicine·2026
Same journal

CastNet: A three-channel EEG-based deep learning model for cross-subject depression detection.

Artificial intelligence in medicine·2026
Same journal

State-of-the-art TinyML approaches for colorectal cancer detection: Current advances, challenges, and future directions.

Artificial intelligence in medicine·2026
Same journal

JRadiEvo: A Japanese radiology report generation model enhanced by evolutionary optimization of model merging.

Artificial intelligence in medicine·2026
Same journal

Causally-informed deep learning towards explainable and generalizable outcome prediction in critical care.

Artificial intelligence in medicine·2026
查看所有相关文章

相关实验视频

Updated: Jun 15, 2025

Metagenomic Analysis of Silage
08:43

Metagenomic Analysis of Silage

Published on: January 13, 2017

18.3K

通过基于压缩的特征来增强元基因组分类.

Jorge Miguel Silva1, João Rafael Almeida1

  • 1IEETA-DETI, LASI, Aveiro University, Aveiro, Portugal.

Artificial intelligence in medicine
|August 22, 2024
PubMed
概括
此摘要是机器生成的。

本研究引入了一种使用数据压缩器进行分类学分类的新型元基因组识别方法. 该方法达到95%的准确性,为分析环境遗传数据提供了一种高效,无引用的解决方案.

关键词:
数据压缩数据的压缩.基因组学就是基因组学.机器学习 机器学习转基因组学是指转基因组学.生物的分类 生物的分类蛋白质组学是指蛋白质组学.序列分类 序列分类 序列分类分类学识别分类学识别.

更多相关视频

Empirical, Metagenomic, and Computational Techniques Illuminate the Mechanisms by which Fungicides Compromise Bee Health
08:36

Empirical, Metagenomic, and Computational Techniques Illuminate the Mechanisms by which Fungicides Compromise Bee Health

Published on: October 9, 2017

9.7K
Single-throughput Complementary High-resolution Analytical Techniques for Characterizing Complex Natural Organic Matter Mixtures
09:38

Single-throughput Complementary High-resolution Analytical Techniques for Characterizing Complex Natural Organic Matter Mixtures

Published on: January 7, 2019

8.6K

相关实验视频

Last Updated: Jun 15, 2025

Metagenomic Analysis of Silage
08:43

Metagenomic Analysis of Silage

Published on: January 13, 2017

18.3K
Empirical, Metagenomic, and Computational Techniques Illuminate the Mechanisms by which Fungicides Compromise Bee Health
08:36

Empirical, Metagenomic, and Computational Techniques Illuminate the Mechanisms by which Fungicides Compromise Bee Health

Published on: October 9, 2017

9.7K
Single-throughput Complementary High-resolution Analytical Techniques for Characterizing Complex Natural Organic Matter Mixtures
09:38

Single-throughput Complementary High-resolution Analytical Techniques for Characterizing Complex Natural Organic Matter Mixtures

Published on: January 7, 2019

8.6K

科学领域:

  • 基因组学和生物信息学
  • 计算生物学 计算生物学

背景情况:

  • 甲基因组学使用下一代测序分析环境DNA.
  • 在元基因组学中,准确的分类学识别是具有挑战性的.
  • 传统的基于参考的方法有其局限性.

研究的目的:

  • 开发一种新的,没有参考的方法,用于元基因组分类学识别.
  • 为了评估数据压缩机的有效性,作为分类的特征.
  • 提高生物体识别在元基因组样本中的准确性和效率.

主要方法:

  • 使用数据压缩器 (通用和基因组特定) 作为分类学分类的特征.
  • 评估了一套全面的数据压缩机.
  • 将该方法应用于具有有限样本类的不平衡数据集.

主要成果:

  • 在分类学识别中实现了95%的整体准确性.
  • 证明了多个压缩机的特征可以增强生物体的识别.
  • 在压缩和分类之间发现了微不足道的相关性,这表明需要采用多方面的方法.

结论:

  • 使用数据压缩器的新方法对于元基因组识别是有效和高效的.
  • 这种无参考方法推动了元基因组学领域的发展.
  • 这些发现为基因组数据的统计和算法特性提供了洞察力.