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Related Concept Videos

Viral Mutations00:36

Viral Mutations

A mutation is a change in the sequence of bases of DNA or RNA in a genome. Some mutations occur during replication of the genome due to errors made by the polymerase enzymes that replicate DNA or RNA. Unlike DNA polymerase, RNA polymerase is prone to errors because it is not capable of “proofreading” its work. Viruses with RNA-based genomes, like HIV, therefore accrue mutations faster than viruses with DNA-based genomes. Because mutation and recombination provide the raw material for adaptive...

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Related Experiment Video

Updated: May 12, 2026

Genotypic Inference of HIV-1 Tropism Using Population-based Sequencing of V3
11:10

Genotypic Inference of HIV-1 Tropism Using Population-based Sequencing of V3

Published on: December 27, 2010

Discovering human immunodeficiency virus mutational pathways using temporal Bayesian networks.

Pablo Hernandez-Leal1, Alma Rios-Flores, Santiago Avila-Rios

  • 1Coordinación de Ciencias Computacionales, Instituto Nacional de Astrofísica, Óptica y Electrónica, Luis Enrique Erro #1, Sta. María Tonantzintla, Puebla, Mexico. pablohl@ccc.inaoep.mx

Artificial Intelligence in Medicine
|April 9, 2013
PubMed
Summary
This summary is machine-generated.

A new Temporal Nodes Bayesian Network (TNBN) model predicts human immunodeficiency virus (HIV) mutation pathways. This tool aids in understanding drug resistance and improving antiretroviral therapy clinical management.

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Amplifying and Quantifying HIV-1 RNA in HIV Infected Individuals with Viral Loads Below the Limit of Detection by Standard Clinical Assays
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Amplifying and Quantifying HIV-1 RNA in HIV Infected Individuals with Viral Loads Below the Limit of Detection by Standard Clinical Assays

Published on: September 26, 2011

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Last Updated: May 12, 2026

Genotypic Inference of HIV-1 Tropism Using Population-based Sequencing of V3
11:10

Genotypic Inference of HIV-1 Tropism Using Population-based Sequencing of V3

Published on: December 27, 2010

Amplifying and Quantifying HIV-1 RNA in HIV Infected Individuals with Viral Loads Below the Limit of Detection by Standard Clinical Assays
13:58

Amplifying and Quantifying HIV-1 RNA in HIV Infected Individuals with Viral Loads Below the Limit of Detection by Standard Clinical Assays

Published on: September 26, 2011

Area of Science:

  • Virology
  • Computational Biology
  • Genetics

Background:

  • Human immunodeficiency virus (HIV) rapidly evolves, developing drug resistance mutations.
  • Understanding the temporal patterns of these mutations is crucial for effective antiretroviral therapy.

Purpose of the Study:

  • To explore probabilistic relationships between antiretroviral drugs and HIV drug resistance mutations.
  • To predict mutational pathways and their temporal sequence of appearance.

Main Methods:

  • Application of a Temporal Nodes Bayesian Network (TNBN) model.
  • Utilized data from the Stanford HIV Drug Resistance Database.
  • Compared TNBN with static Bayesian networks, dynamic Bayesian networks, and association rules.

Main Results:

  • TNBN demonstrated a 64.2% sparser structure compared to static networks.
  • The model accurately captured known drug-resistance mutation pathways.
  • Achieved a predictive accuracy of 90.5% in associating antiretroviral drugs with mutations.

Conclusions:

  • TNBN offers a valuable tool for studying drug-mutation and mutation-mutation networks in HIV.
  • Potential to significantly impact clinical management of patients on antiretroviral therapy.
  • Opens new avenues for predicting HIV evolution for drug development and treatment planning.