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相关概念视频

Evolutionary Relationships through Genome Comparisons02:54

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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...
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Neural Circuits01:25

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Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
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Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
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Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...
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Metal ions can be separated from one another by complexation with organic ligands–the chelating agent– to form uncharged chelates. Here, the chelating agent must contain hydrophobic groups and behave as a weak acid, losing a proton to bind with the metal. Since most organic ligands used in this process are insoluble or undergo oxidation in the aqueous phase, the chelating agent is initially added to the organic phase and extracted into the aqueous phase. The metal-ligand complex is...
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迪普金:预测低覆盖基因组和古基因组与卷积神经网络的相关性

Merve N Güler1, Ardan Yılmaz2, Büşra Katırcıoğlu1

  • 1Department of Biological Sciences, Middle East Technical University, Ankara, Türkiye.

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概括

使用卷积神经网络 (CNN) 的新工具DeepKin,可以从有限的基因组数据准确预测遗传相关性. 它的性能与现有方法相比,为古代DNA和法医遗传学提供了强大的解决方案.

关键词:
古代的 DNA DNA 古代的 DNA DNA生物信息学/植物信息学卷积神经网络是一种卷积神经网络.深度学习是一种深度学习.古遗传学是古遗传学.

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科学领域:

  • 基因组学就是基因组学.
  • 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:

  • 在各种领域估计遗传亲属关系至关重要.
  • 传统方法面临的挑战是有限或退化的基因组数据,如古遗传组和法医样本.
  • 卷积神经网络 (CNN) 为高级基因组数据分析提供了潜力.

研究的目的:

  • 介绍DeepKin,这是一个用于使用CNN预测遗传亲属关系的新工具.
  • 解决处理稀疏基因组数据的现有方法的局限性.
  • 为了分类到第三度的亲属关系,并确定亲密的家族对.

主要方法:

  • 开发两种CNN模型,这些模型完全基于仿真基因组数据进行训练.
  • 输入数据格式:PLINK的 .map 和 .ped 文件.
  • 与READv2工具进行比较,并对实证古遗传学数据集进行验证.

主要成果:

  • DeepKin 的性能与 READv2.2 的性能相当或更高.
  • 在各种基因背景中表现出强度和适应性.
  • 在古遗传学数据上与超过10,000个共享SNP实现了>90%的准确性.

结论:

  • DeepKin为相关性估计提供了一种新的方法方法,特别是在降解样本.
  • 该工具适用于古代DNA,法医遗传学和保护遗传学.
  • 在具有挑战性的基因组场景中,CNN显示出有前途的推进相关性预测.