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

Histone Variants at the Centromere02:30

Histone Variants at the Centromere

5.1K
Histone variants are the histone proteins with structural and sequence variations. These variants may be regarded as “mutant” forms that replace their canonical histone counterparts in the nucleosomes. Specific post-translational modifications on the histone variants enable further chromatin complexity and regulate tissue-specific gene expression. The most common histone variants are from histone H2A, H2B, and linker histone H1 families. However, several variants of histone H3...
5.1K
Passive Filters01:27

Passive Filters

1.0K
Passive filters are utilized to shape the frequency spectrum of signals across a diverse array of applications. These filters, using only passive elements like resistors (R), inductors (L), and capacitors (C), are capable of selectively allowing or blocking certain frequency ranges without the need for external power sources.
Low-Pass Filters
Low-pass filters are designed to transmit signals with frequencies lower than the cutoff frequency, ωc, and attenuate those above it. The cutoff...
1.0K
Active Filters01:25

Active Filters

1.3K
Active filters are electronic circuits that use operational amplifiers (op-amps), resistors, and capacitors to filter out unwanted frequency components from a signal. A first-order low-pass active filter is designed to pass signals with a frequency lower than a certain cutoff frequency and attenuate frequencies higher than that cutoff frequency. The transfer function for a first-order low-pass active filter is:
1.3K
Machines01:19

Machines

581
Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. One example of a machine is the cutting plier, which is used to cut wires by applying forces to its handles. When equal and opposite forces are exerted on the handles of the cutting plier, they cause the cutting edges to come together and apply equal and opposite reaction forces on the wire, which are greater than the applied forces.
A free-body diagram of the...
581
Random and Systematic Errors01:20

Random and Systematic Errors

15.2K
Scientists always try their best to record measurements with the utmost accuracy and precision. However, sometimes errors do occur. These errors can be random or systematic. Random errors are observed due to the inconsistency or fluctuation in the measurement process, or variations in the quantity itself that is being measured. Such errors fluctuate from being greater than or less than the true value in repeated measurements. Consider a scientist measuring the length of an earthworm using a...
15.2K
Systematic Sampling Method01:17

Systematic Sampling Method

13.4K
Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
Systematic sampling is one of the simplest methods...
13.4K

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

Harmonizing standards and resources for the medical genome.

Nature·2026
Same author

Evolutionary dynamics of Respiratory Syncytial Virus in pre-pandemic, pandemic, and post-pandemic periods in Houston, Texas, USA.

bioRxiv : the preprint server for biology·2026
Same author

Structural variant calling using Sniffles2.

Nature protocols·2026
Same author

A complete human pancreatic cancer genome.

bioRxiv : the preprint server for biology·2026
Same author

Rapid phylogenomic analysis for viral surveillance and metagenomic profiling with Omni2Tree.

bioRxiv : the preprint server for biology·2026
Same author

A computational model for quantifying instability of tandem repeats across the genome.

bioRxiv : the preprint server for biology·2026

相关实验视频

Updated: Feb 10, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.8K

系统评估机器学习用于结构变量过的系统评估.

Archit Kalra, Luis F Paulin, Fritz J Sedlazeck

    bioRxiv : the preprint server for biology
    |February 9, 2026
    PubMed
    概括

    对于长读序列中的结构变体 (SV) 检测,简单的随机森林模型提供了最佳的准确性和速度. 复杂的深度学习和基础模型并不能显著提高生殖系SV过的性能.

    科学领域:

    • 基因组学就是基因组学.
    • 生物信息学是一种生物信息学.
    • 机器学习 机器学习

    背景情况:

    • 从长读测序中准确识别结构变体 (SV) 是一个挑战.
    • 现有的机器学习 (ML) 方法缺乏系统的性能比较.
    • 深度学习和基础模型越来越多地应用于 SV 分析.

    研究的目的:

    • 为了对SV过的各种ML范式进行基准测试.
    • 在标准化数据上评估经典,深度学习和基础模型.
    • 为选择SV检测方法提供一个框架.

    主要方法:

    • 五个ML范式的全面基准:随机森林,计算机视觉模型 (ResNet/VICReg),基于扩散的异常检测,Evo2-7B上的稀疏自动编码器 (SAE) 和合集.
    • 使用了HG002和HG005样本的标准化"瓶装基因组" (GIAB) 数据.
    • 基于准确性,效率和可解释性的评估模型.

    主要成果:

    • 一个简单的随机森林分类器达到95.7%的峰值F1得分,与ResNet50 (95.9%) 和扩散模型 (95.8%) 等复杂模型相美.
    • 扩散模型和SAE在表示学习和特征解释性方面表现出前景,但没有超过性能上限.

    更多相关视频

    Constructing and Visualizing Models using Mime-based Machine-learning Framework
    06:19

    Constructing and Visualizing Models using Mime-based Machine-learning Framework

    Published on: July 22, 2025

    2.6K
    A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
    12:18

    A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

    Published on: January 11, 2020

    8.1K

    相关实验视频

    Last Updated: Feb 10, 2026

    Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
    04:09

    Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

    Published on: October 10, 2018

    8.8K
    Constructing and Visualizing Models using Mime-based Machine-learning Framework
    06:19

    Constructing and Visualizing Models using Mime-based Machine-learning Framework

    Published on: July 22, 2025

    2.6K
    A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
    12:18

    A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

    Published on: January 11, 2020

    8.1K
  • 在本研究中,组合方法没有提供任何性能优势.
  • 结论:

    • 简单的,可解释的ML模型提供了精度,速度和透明度的最佳平衡,用于生殖线SV过.
    • 增加模型复杂性只能在满足未满足的生物需求的情况下进行.
    • 这一基准有助于为 SV 分析选择务实的方法.