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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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DNA as a Genetic Template02:05

DNA as a Genetic Template

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Two structural features of the DNA molecule provide a basis for the mechanisms of heredity: the four nucleotide bases and its double-stranded nature. The Watson-Crick model of double-helical DNA structure, proposed in 1952, drew heavily upon the X-ray crystallography work of researchers Rosalind Franklin and Maurice Wilkins. Watson, Crick, and Wilkins jointly received the Nobel Prize in Physiology or Medicine for their work in 1962. Franklin was, controversially, excluded from the prize for...
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Multi-species Conserved Sequences02:51

Multi-species Conserved Sequences

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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.
Although the genome of each species varies greatly from each other, a few sequences are highly conserved. Such conserved...
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Nucleic Acid Structure01:25

Nucleic Acid Structure

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The pentose sugar in DNA is deoxyribose, while in RNA the pentose sugar is ribose. The difference between the sugars is the presence of the hydroxyl group on the ribose's second carbon and a hydrogen on the deoxyribose's second carbon. The phosphate residue attaches to the hydroxyl group of the 5′ carbon of one sugar and the hydroxyl group of the 3′ carbon of the sugar of the next nucleotide, which forms  a 5′ to 3′ phosphodiester linkage.
DNA Structure
DNA...
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DNA Microarrays02:34

DNA Microarrays

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Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...
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The Nucleosome01:19

The Nucleosome

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Human DNA is almost two meters long. However, it is compressed inside a tiny nucleus measuring only a few microns in diameter. To make this degree of compaction possible, DNA is organized into several sequential levels so that it can fit into such a tiny space. The most compact form of DNA is a chromosome that can be seen under a microscope in a dividing cell.
In a chromosome, DNA is wound twice around a protein complex called a histone octamer core, which consists of 8 histone proteins. This...
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相关实验视频

Updated: Jun 4, 2025

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
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一个稀疏而广泛的神经网络模型用于DNA序列.

Tong Yu1, Lei Cheng1, Ruslan Khalitov1

  • 1Norwegian University of Science and Technology, Trondheim, Norway.

Neural networks : the official journal of the International Neural Network Society
|December 22, 2024
PubMed
概括
此摘要是机器生成的。

SwanDNA是一种新型的神经网络,使用稀疏,广的架构和自我监督学习,准确地模拟长 DNA 序列. 这种方法需要更少的标签来准确预测基因组任务.

关键词:
DNA 序列建模模型神经网络的神经网络的神经网络自主监督的预训.

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Analyzing and Building Nucleic Acid Structures with 3DNA
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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:

  • 精确的DNA序列建模对于理解生物功能至关重要.
  • 现有的深度神经网络在长距离交互方面扎,需要大量的标记数据.

研究的目的:

  • 开发一种新的神经网络模型,SwanDNA,能够建模长DNA序列.
  • 克服现有模型在捕捉远程语义关系方面的局限性,减少对监督标签的依赖.

主要方法:

  • 利用稀疏而宽泛的网络架构来高效处理长DNA序列.
  • 整合了一个自我监督的学习框架,以尽量减少对监督标签的需求.

主要成果:

  • 天DNA在预测人类变异效应和检测植物基因中的开放色素区域方面表现出卓越的表现.
  • 在8个基因组基准数据集中的7个中取得了最先进的结果.
  • 在非常长的DNA序列上成功启用了推断.

结论:

  • 斯旺DNA为DNA序列建模提供了强大而高效的解决方案,特别是对于长序列.
  • 自主监督的方法显著降低了与获得标记数据相关的实际成本.
  • 该模型在各种任务中的性能突显了其在基因组应用中的多功能性和有效性.