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Related Concept Videos

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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Related Experiment Video

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

Digenic variant interpretation with hypothesis-driven explainable AI.

Federica De Paoli1, Giovanna Nicora1, Silvia Berardelli1,2

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

NAR Genomics and Bioinformatics
|March 31, 2025
PubMed
Summary
This summary is machine-generated.

Digenic inheritance can improve rare disease diagnosis. Our tool, diVas, uses machine learning to identify gene pairs causing disease, achieving high accuracy and explaining the mechanisms.

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Last Updated: Jun 19, 2026

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

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Published on: January 16, 2019

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05:47

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

Published on: June 13, 2025

Area of Science:

  • Genetics
  • Bioinformatics
  • Computational Biology

Background:

  • Rare diseases often present complex diagnostic challenges.
  • The digenic inheritance hypothesis suggests two genes acting together can cause disease.
  • Current diagnostic methods struggle to efficiently identify digenic causes.

Purpose of the Study:

  • To develop a computational tool for interpreting and prioritizing digenic variant combinations.
  • To improve the diagnostic yield for rare diseases using a machine learning approach.
  • To leverage explainable AI for understanding digenic disease mechanisms.

Main Methods:

  • Developed diVas, a hypothesis-driven machine learning approach.
  • Interpreted genomic variants across gene pairs, integrating phenotype and family data.
  • Validated performance on 11 clinical cases and 645 published digenic combinations.

Main Results:

  • DiVas achieved 73% sensitivity and a median rank of 3 for causative digenic combinations in clinical cases.
  • Demonstrated a sensitivity of 0.81 across 645 published digenic combinations.
  • Successfully utilized explainable AI to elucidate digenic disease mechanisms.

Conclusions:

  • DiVas significantly enhances the diagnostic process for rare diseases by accurately identifying digenic inheritance patterns.
  • The tool provides valuable insights into complex genetic interactions underlying rare conditions.
  • Explainable AI in diVas aids in understanding the biological basis of digenic diseases.