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相关概念视频

Incomplete Dominance01:43

Incomplete Dominance

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.
Exon Recombination02:32

Exon Recombination

The evolution of new genes is critical for speciation. Exon recombination, also known as exon shuffling or domain shuffling, is an important means of new gene formation. It is observed across vertebrates, invertebrates, and in some plants such as potatoes and sunflowers. During exon recombination, exons from the same or different genes recombine and produce new exon-intron combinations, which might evolve into new genes. 
Exon shuffling follows “splice frame rules.” Each exon has three reading...
Experimental RNAi02:15

Experimental RNAi

RNA interference (RNAi) is a cellular mechanism that inhibits gene expression by suppressing its transcription or activating the RNA degradation process. The mechanism was discovered by Andrew Fire and Craig Mello in 1998 in plants. Today, it is observed in almost all eukaryotes, including protozoa, flies, nematodes, insects, parasites, and mammals. This precise cellular mechanism of gene silencing has been developed into a technique that provides an efficient way to identify and determine the...
Principles of Pharmacogenetics: Types of Genetic Variants01:27

Principles of Pharmacogenetics: Types of Genetic Variants

The human genome is over 99.9% identical between individuals, yet genetic differences exist at millions of bases. The human genome contains approximately 3 million variant positions per individual, many of which are heterozygous, contributing to genetic diversity and individual traits. Genetic variations include single-nucleotide polymorphisms (SNPs), insertions, deletions, and copy number variations (CNVs).SNPs, the most common variation, involve single-base changes in DNA. These can be...

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相关实验视频

Updated: Jun 19, 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

用假设驱动的可解释AI解释Digenic变体的解释.

Federica De Paoli1, Giovanna Nicora1, Silvia Berardelli1,2

  • 1enGenome Srl, Via Ferrata 5, 27100, Pavia, Italy.

NAR genomics and bioinformatics
|March 31, 2025
PubMed
概括
此摘要是机器生成的。

基因遗传可以改善罕见疾病的诊断. 我们的工具,diVas,使用机器学习来识别引起疾病的基因对,实现高精度和解释机制.

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

Published on: August 20, 2019

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

相关实验视频

Last Updated: Jun 19, 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

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

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

科学领域:

  • 遗传学 是一个遗传学.
  • 生物信息学是一种生物信息学.
  • 计算生物学 计算生物学

背景情况:

  • 罕见疾病往往会带来复杂的诊断挑战.
  • 二基因遗传假说表明,两个共同作用的基因可以导致疾病.
  • 目前的诊断方法很难有效地确定基因原因.

研究的目的:

  • 开发一种用于解释和优先考虑二基变异组合的计算工具.
  • 用机器学习方法提高罕见疾病的诊断产量.
  • 利用可解释的人工智能来理解基因疾病机制.

主要方法:

  • 开发了diVas,一种基于假设的机器学习方法.
  • 解释了跨基因对的基因变异,整合了表型和家族数据.
  • 在11个临床病例和645个已发表的二基因组合中验证了性能.

主要成果:

  • 在临床病例中,DiVas获得了73%的灵敏度和对致病性二基因组合的中位数排名为3.
  • 在645个已发表的二基因组合中表现出0.81的灵敏度.
  • 成功利用可解释的人工智能来阐明基因疾病机制.

结论:

  • 通过准确识别基因遗传模式,DiVas显著提高了罕见疾病的诊断过程.
  • 该工具提供了对罕见疾病背后的复杂遗传相互作用的宝贵见解.
  • 在diVas中可解释的AI有助于理解基因疾病的生物学基础.