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相关概念视频

Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

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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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Genomics02:02

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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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The genome refers to all of the genetic material in an organism. It can range from a few million base pairs in microbial cells to several billion base pairs in many eukaryotic organisms. Genome assembly refers to the process of taking the DNA sequencing data and putting it all back together in a correct order to create a close representation of the original genome. This is followed by the identification of functional elements on the newly assembled genome, a process called genome annotation.
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The first human genome sequencing project cost $2.7 billion and was declared complete in 2003, after 15 years of international cooperation and collaboration between several research teams and funding agencies. Today, with the advent of next-generation sequencing technologies, the cost and time of sequencing a human genome have dropped over 100 fold.
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Next-generation sequencing technologies have created large genomic databases of a variety of animals and plants. Ever since the human genome project was completed, scientists studied the genome of primates, mammals, and other phylogenetically distant living beings. Such large-scale  studies have provided new insights into the evolutionary relationship between organisms.
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一个具有生物学知识和高效的DNA序列学习器,用于预测功能性基因组学事件.

Mohammad Shiri1, Jiangwen Sun1

  • 1Department of Computer Science, Old Dominion University, Norfolk, Virginia, USA.

Journal of computational biology : a journal of computational molecular cell biology
|September 24, 2025
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种高效的DNA序列学习器 (EDSL),以提高对基因组-现象联系的理解. 这种新的架构增强了DNA序列模式学习,以便更好地进行功能性基因组预测.

关键词:
DNA 序列学习学习在DenseNet中,使用的是DenseNet.深度神经网络是一个神经网络.功能性基因组事件预测和预测.多任务学习是多任务学习.

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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:

  • 全基因组关联研究 (GWAS) 确定了基因-表型联系,但功能机制仍然不清楚.
  • 深度神经网络显示出将DNA序列映射到功能性基因组事件的前景.
  • 现有的深度学习模型使用统一的过器大小,限制了各种DNA模式的学习效率.

研究的目的:

  • 为增强DNA序列学习开发一种新的,生物知情的深度学习架构.
  • 从DNA序列改进功能性基因组事件的预测.
  • 在DNA序列分析中克服现有的卷积神经网络架构的局限性.

主要方法:

  • 提出了一种具有新架构的高效DNA序列学习器 (EDSL).
  • 在初始卷积层中嵌入了不同尺寸的过器,以捕获不同的序列模式.
  • 利用密集的连接来整合多个级别的序列模式进行预测.

主要成果:

  • 与现有网络相比,EDSL表现出优越的预测性能和序列模式学习.
  • 结果在合成数据和367个功能性基因组资料的数据集上得到了验证.
  • 除研究证实,不同尺寸的过器和密集的连接差异性和互补性增强学习.

结论:

  • 提出的EDSL架构有效地解决了当前用于DNA序列分析的深度学习方法的局限性.
  • 新的设计选择提高了学习复杂序列模式和预测功能性基因组事件的能力.
  • 这项工作为阐明基因组学中的功能机制提供了更有效,更准确的工具.