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

Pleiotropy01:33

Pleiotropy

37.9K
Pleiotropy is the phenomenon in which a single gene impacts multiple, seemingly unrelated phenotypic traits. For example, defects in the SOX10 gene cause Waardenburg Syndrome Type 4, or WS4, which can cause defects in pigmentation, hearing impairments, and an absence of intestinal contractions necessary for elimination. This diversity of phenotypes results from the expression pattern of SOX10 in early embryonic and fetal development. SOX10 is found in neural crest cells that form melanocytes,...
37.9K
Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

12.1K
Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...
12.1K
Multiple Allele Traits01:49

Multiple Allele Traits

33.8K
The Concept of Multiple Allelism
33.8K
Incomplete Dominance01:43

Incomplete Dominance

20.3K
Gregor Mendel's work (1822 - 1884) was primarily focused on pea plants. Through his initial experiments, he determined that every gene in a diploid cell has two variants called alleles inherited from each parent. He suggested that amongst these two alleles, one allele is dominant in character and the other recessive. The combination of alleles determines the phenotype of a gene in an organism.
20.3K
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
What is Population Genetics?01:25

What is Population Genetics?

56.9K
A population is composed of members of the same species that simultaneously live and interact in the same area. When individuals in a population breed, they pass down their genes to their offspring. Many of these genes are polymorphic, meaning that they occur in multiple variants. Such variations of a gene are referred to as alleles. The collective set of all the alleles within a population is known as the gene pool.
56.9K

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

Onyx Migration to the Middle Cerebral Artery Treated by Mechanical Thrombectomy with Subsequent Delayed Migration of Residual Onyx: A Case Report.

Annals of vascular diseases·2026
Same author

Atrial natriuretic peptide can be biomarker for predicting atrial fibrillation in embolic stroke of undetermined source.

Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology·2026
Same author

Germline Cancer Testing in Unselected Patients With Neuroendocrine Neoplasms: A Multi-center Prospective Study.

Pancreas·2026
Same author

Sensitivity of HiFi long-read genome sequencing for difficult-to-detect pathogenic variants when applied to real-world clinical laboratory samples.

American journal of human genetics·2026
Same author

Pineal Hemorrhage After Alteplase.

Internal medicine (Tokyo, Japan)·2026
Same author

A Case of Hydrophilic Polymer Embolism after Transcatheter Aortic Valve Replacement.

International heart journal·2026

相关实验视频

Updated: May 10, 2025

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations
08:03

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Published on: December 7, 2021

2.0K

利用基因型和表型数据进行人口规模变异分类,使用大型语言模型和贝叶斯推理.

Toby R Manders1, Christopher A Tan2, Yuya Kobayashi2

  • 1Labcorp Genetics Inc, 1400 16th Street, San Francisco, CA, 94103, USA. toby.manders@labcorp.com.

Human genetics
|April 23, 2025
PubMed
概括

一种新的机器学习方法有效地利用患者数据来改善遗传变异分类,在遗传性疾病测试中显著减少不确定的意义 (VUS) 的变异,并帮助临床决策.

更多相关视频

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay EMSA and DNA-affinity Precipitation Assay DAPA
11:35

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay EMSA and DNA-affinity Precipitation Assay DAPA

Published on: August 21, 2016

12.9K
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

10.9K

相关实验视频

Last Updated: May 10, 2025

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations
08:03

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Published on: December 7, 2021

2.0K
Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay EMSA and DNA-affinity Precipitation Assay DAPA
11:35

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay EMSA and DNA-affinity Precipitation Assay DAPA

Published on: August 21, 2016

12.9K
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

10.9K

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.
  • 机器学习 机器学习

背景情况:

  • 不确定意义的变异 (VUS) 在遗传性疾病的遗传检测中构成挑战.
  • 由于缺乏可扩展的策略,临床数据的不足利用阻碍了VUS的减少.

研究的目的:

  • 通过基因型和表型数据评估机器学习方法,以改善变异分类和减少VUS.
  • 确定机器学习是否可以利用未充分利用的临床数据来更准确地解释变异.

主要方法:

  • 开发了一种多步骤的机器学习模型,使用来自测试要求表格的患者数据来生成"患者得分"和"变异得分"用于致病性推断.
  • 该研究包括350万名患者,模型评估了歧视,分类性能和与其他致病性措施的一致性.
  • 临床变异模型 (CVM) 被整合到分类框架中,对高可信度预测进行专家审查.

主要成果:

  • 在1334个开发的临床变异模型 (CVM) 中,595个显示出高性能 (AUROC患者≥0.8和AUROC变异≥0.8).
  • 高可信度CVM预测在200,174名患者中提供了5362个VUS的证据,解决了研究基因中23.4%的VUS观察.
  • 在17个频繁测试的基因中,CVM重新分类了超过1000个独特的VUS,每种疾病的VUS报告率降低了9-49%.

结论:

  • 一种可扩展的机器学习方法有效地使用未充分利用的临床数据来改善遗传变异分类.
  • 这种方法显著降低了不确定的意义 (VUS) 的变异率,提高了遗传性疾病遗传检测的实用性.