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

Proteomics01:33

Proteomics

9.3K
A proteome is the entire set of proteins that a cell type produces. We can study proteomes using the knowledge of genomes because genes code for mRNAs, and the mRNAs encode proteins. Although mRNA analysis is a step in the right direction, not all mRNAs are translated into proteins.
Proteomics is the study of proteomes' function. It involves the large-scale systematic study of the proteome to denote the protein complement expressed by a genome. Scientist Mark Wilkins coined the term...
9.3K

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

Rare protein-coding variation and the genetic architecture of height in >1.4 million individuals.

medRxiv : the preprint server for health sciences·2026
Same author

Type-2-Inflammatory-Diseases Share Comorbidities, Molecular Signatures, IL4/IL13 Genetics, and Response to IL4/IL13 Blockade.

Allergy·2026
Same author

Polygenic risk score and 20-year prostate cancer-specific mortality and survival.

Communications medicine·2026
Same author

Population-scale repeat expansions elucidate disease risk and brain atrophy.

Nature·2026
Same author

Humans with function-disrupting variants in the myostatin gene (MSTN) have increased skeletal muscle mass and strength, and less adiposity.

Nature communications·2026
Same author

Rare coding variants in CHRNB3 associate with reduced daily cigarette smoking across ancestries.

Nature communications·2026

相关实验视频

Updated: Jan 12, 2026

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation
07:15

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation

Published on: January 16, 2019

11.3K

使用蛋白质学信息的大型语言模型的变异分类增加了罕见变异关联研究的力量,并增强了目标发现.

Christopher E Gillies1, Joelle Mbatchou1, Lukas Habegger1

  • 1Regeneron Genetics Center, Tarrytown, New York, USA.

Genetic epidemiology
|November 3, 2025
PubMed
概括

使用蛋白质组学数据改进了用于预测有害遗传变异的大型语言模型 (LLM). 这种精细的方法增强了在人类遗传研究中发现基因特征关联的发现.

更多相关视频

Identification and Classification of Position-specific GABAA Receptor Subunit Missense Variants for Their Role In Hippocampal Pyramidal Neurons
08:04

Identification and Classification of Position-specific GABAA Receptor Subunit Missense Variants for Their Role In Hippocampal Pyramidal Neurons

Published on: June 6, 2025

1.3K
In Vivo Functional Study of Disease-associated Rare Human Variants Using Drosophila
06:41

In Vivo Functional Study of Disease-associated Rare Human Variants Using Drosophila

Published on: August 20, 2019

14.2K

相关实验视频

Last Updated: Jan 12, 2026

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation
07:15

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation

Published on: January 16, 2019

11.3K
Identification and Classification of Position-specific GABAA Receptor Subunit Missense Variants for Their Role In Hippocampal Pyramidal Neurons
08:04

Identification and Classification of Position-specific GABAA Receptor Subunit Missense Variants for Their Role In Hippocampal Pyramidal Neurons

Published on: June 6, 2025

1.3K
In Vivo Functional Study of Disease-associated Rare Human Variants Using Drosophila
06:41

In Vivo Functional Study of Disease-associated Rare Human Variants Using Drosophila

Published on: August 20, 2019

14.2K

科学领域:

  • 遗传学 遗传学 是一个
  • 生物信息学是一种生物信息学.
  • 蛋白质组学是指蛋白质组学.

背景情况:

  • 罕见变异关联分析对于理解人类生物学至关重要.
  • 预测遗传变异的影响对于这种分析至关重要.
  • 深度学习和大型语言模型 (LLM) 在变量影响预测方面表现有前途.

研究的目的:

  • 使用蛋白质组学数据评估和完善有害遗传变异的LLM预测因素.
  • 评估蛋白质学引导的LLM在识别基因特征关联方面的表现.

主要方法:

  • 利用来自2898种蛋白质的46665个个体的蛋白质组学数据.
  • 开发和完善基于LLM的变体预测器.
  • 评估了241个阳性对照基因特征对和10个英国生物库特征的模型性能.

主要成果:

  • 蛋白质学引导的LLM在识别有害的误解变体方面超过了传统和机器学习方法.
  • 该模型总结了已知的36.5%的基因特征关联,超过了其他选择.
  • 177个新的基因特征关联被确定使用英国生物库数据模型.

结论:

  • 蛋白质组学数据可以有效地改进LLM变体分类.
  • 这种方法显著提高了人类遗传研究的发现潜力.