Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Viral Mutations00:36

Viral Mutations

32.4K
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...
32.4K
Gene Evolution - Fast or Slow?02:05

Gene Evolution - Fast or Slow?

7.2K
The genomes of eukaryotes are punctuated by long stretches of sequence which do not code for proteins or RNAs. Although some of these regions do contain crucial regulatory sequences, the vast majority of this DNA serves no known function. Typically, these regions of the genome are the ones in which the fastest change, in evolutionary terms, is observed, because there is typically little to no selection pressure acting on these regions to preserve their sequences.
In contrast, regions which code...
7.2K
Single Nucleotide Polymorphisms-SNPs01:05

Single Nucleotide Polymorphisms-SNPs

15.2K
A single nucleotide polymorphism or SNP is a single nucleotide variation at a specific genomic position in a large population. It is the most prevalent type of sequence variation found in the human genome. Point mutations that occur in more than 1% of the population qualify as SNPs. These are present once every 1000 nucleotides on an average in the human genome. Replacement of a purine with another purine (A/G) or a pyrimidine with another pyrimidine (C/T) is known as a transition. In contrast,...
15.2K
Point and Frameshift Mutations01:30

Point and Frameshift Mutations

33
Point mutations are genetic alterations involving the change of a single nucleotide base pair in DNA. Depending on how the alteration affects protein synthesis, they can lead to various consequences.Point mutations fall into the following types:Silent mutations occur when a nucleotide change does not alter the amino acid sequence due to the redundancy of the genetic code. For instance, changing ACC to ACA still encodes threonine, leaving the protein function unaffected. This occurs because...
33
Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

5.8K
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...
5.8K
Mutation, Gene Flow, and Genetic Drift01:09

Mutation, Gene Flow, and Genetic Drift

58.5K
In a population that is not at Hardy-Weinberg equilibrium, the frequency of alleles changes over time. Therefore, any deviations from the five conditions of Hardy-Weinberg equilibrium can alter the genetic variation of a given population. Conditions that change the genetic variability of a population include mutations, natural selection, non-random mating, gene flow, and genetic drift (small population size).
58.5K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

A non-sub-sampled shearlet transform-based deep learning sub band enhancement and fusion method for multi-modal images.

Scientific reports·2025
Same author

Exploring coronavirus sequence motifs through convolutional neural network for accurate identification of COVID-19.

Computer methods in biomechanics and biomedical engineering·2024
Same author

Texture based feature extraction using symbol patterns for facial expression recognition.

Cognitive neurodynamics·2024
Same author

A sequence-based two-layer predictor for identifying enhancers and their strength through enhanced feature extraction.

Journal of bioinformatics and computational biology·2022
Same author

PPred-PCKSM: A multi-layer predictor for identifying promoter and its variants using position based features.

Computational biology and chemistry·2022
Same author

NoAS-DS: Neural optimal architecture search for detection of diverse DNA signals.

Neural networks : the official journal of the International Neural Network Society·2022

Related Experiment Video

Updated: Jul 17, 2025

Isolation of Fidelity Variants of RNA Viruses and Characterization of Virus Mutation Frequency
18:10

Isolation of Fidelity Variants of RNA Viruses and Characterization of Virus Mutation Frequency

Published on: June 16, 2011

29.6K

Coot-Lion optimized deep learning algorithm for COVID-19 point mutation rate prediction using genome sequences.

Praveen Gugulothu1, Raju Bhukya1

  • 1Department of Computer Science and Engineering, National Institute of Technology Warangal, Hanamkonda, Telangana 506004, India.

Computer Methods in Biomechanics and Biomedical Engineering
|September 5, 2023
PubMed
Summary
This summary is machine-generated.

A novel deep quantum neural network (DQNN) using the Lion-based Coot algorithm (LBCA) accurately predicts COVID-19 from genomic data. This LBCA-based DQNN shows high performance in identifying viral mutations for reliable disease forecasting.

Keywords:
Bray-Curtis distanceCOVID-19 predictionGenome sequencesdeep belief networkdeep quantum neural network

More Related Videos

Rare Event Detection Using Error-corrected DNA and RNA Sequencing
10:36

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Published on: August 3, 2018

12.1K
DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning
04:17

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

Published on: May 10, 2024

795

Related Experiment Videos

Last Updated: Jul 17, 2025

Isolation of Fidelity Variants of RNA Viruses and Characterization of Virus Mutation Frequency
18:10

Isolation of Fidelity Variants of RNA Viruses and Characterization of Virus Mutation Frequency

Published on: June 16, 2011

29.6K
Rare Event Detection Using Error-corrected DNA and RNA Sequencing
10:36

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Published on: August 3, 2018

12.1K
DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning
04:17

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

Published on: May 10, 2024

795

Area of Science:

  • Computational biology
  • Genomics
  • Artificial intelligence

Background:

  • Accurate COVID-19 prediction is crucial for public health management.
  • Genomic surveillance plays a vital role in tracking viral evolution and predicting outbreaks.
  • Integrating advanced computational models can enhance prediction accuracy.

Purpose of the Study:

  • To develop and evaluate a deep quantum neural network (DQNN) model for COVID-19 prediction.
  • To utilize genomic sequences and mutation points for enhanced prediction capabilities.
  • To assess the performance of a novel Lion-based Coot algorithm (LBCA) integrated with DQNN.

Main Methods:

  • Feature extraction from COVID-19 genome sequences.
  • Feature fusion using Bray-Curtis distance and deep belief networks (DBN).
  • Implementation of a Lion-based Coot algorithm (LBCA) by integrating Coot algorithm and LOA.
  • COVID-19 prediction using the LBCA-based deep quantum neural network (DQNN) focusing on mutation points.

Main Results:

  • The LBCA-based Deep QNN achieved a testing accuracy of 0.941.
  • The model demonstrated a true positive rate of 0.931.
  • A false positive rate of 0.869 was recorded, indicating high specificity.

Conclusions:

  • The LBCA-based Deep QNN is a highly effective tool for COVID-19 prediction.
  • The model's performance highlights the potential of quantum machine learning in infectious disease forecasting.
  • Genomic feature analysis combined with advanced algorithms offers a promising approach for pandemic preparedness.