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相关概念视频

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

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相关实验视频

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

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库特-狮子优化深度学习算法用于COVID-19点突变率预测,使用基因组序列.

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
概括
此摘要是机器生成的。

使用基于Lion的Coot算法 (LBCA) 的新型深度量子神经网络 (DQNN) 从基因组数据准确地预测了COVID-19. 这种基于LBCA的DQNN在识别病毒突变以可靠预测疾病方面表现出很高的性能.

关键词:
布雷 - 库蒂斯距离的距离预测COVID-19的预测情况基因组序列的测序深信网络是一个深信网络.深度量子神经网络是一个深度量子神经网络.

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Rare Event Detection Using Error-corrected DNA and RNA Sequencing
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DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning
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相关实验视频

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

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Rare Event Detection Using Error-corrected DNA and RNA Sequencing
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Rare Event Detection Using Error-corrected DNA and RNA Sequencing

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DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning
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科学领域:

  • 计算生物学是一种计算生物学.
  • 基因组学就是基因组学.
  • 人工智能的人工智能是人工智能.

背景情况:

  • 准确的COVID-19预测对于公共卫生管理至关重要.
  • 基因组监测在跟踪病毒演变和预测疫情方面发挥着至关重要的作用.
  • 集成先进的计算模型可以提高预测准确性.

研究的目的:

  • 开发和评估用于COVID-19预测的深度量子神经网络 (DQNN) 模型.
  • 利用基因组序列和突变点来增强预测能力.
  • 评估与DQNN集成的基于Lion的新型Coot算法 (LBCA) 的性能.

主要方法:

  • 从COVID-19基因组序列中提取特征.
  • 使用布雷-库蒂斯距离和深信网络 (DBN) 的特征融合.
  • 实现基于Lion的Coot算法 (LBCA) 通过整合Coot算法和LOA.
  • 使用基于LBCA的深度量子神经网络 (DQNN) 专注于突变点的COVID-19预测.

主要成果:

  • 基于LBCA的深度QNN实现了0.941.9的测试准确度.
  • 该模型显示真正阳性率为0.931.
  • 记录了0.869的错误阳性率,表明高特异性.

结论:

  • 基于LBCA的深度QNN是COVID-19预测的高效工具.
  • 该模型的性能突显了量子机器学习在传染病预测中的潜力.
  • 基因组特征分析与先进的算法相结合,为流行病准备提供了一个有希望的方法.