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相关概念视频

Structural Protein Function01:56

Structural Protein Function

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Structural proteins are a category of proteins responsible for functions ranging from cell shape and movement to providing support to major structures such as bones, cartilage, hair, and muscles. This group includes proteins such as collagen, actin, myosin, and keratin.
Collagen, the most abundant protein in mammals, is found throughout the body. In connective tissue, such as skin, ligaments, and tendons, it provides tensile strength and elasticity.  In bones and teeth, it mineralizes to...
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Genome Annotation and Assembly03:36

Genome Annotation and Assembly

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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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Next-generation Sequencing03:00

Next-generation Sequencing

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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.
Next-Generation Sequencing Methods
Although all next-generation methods use different technologies, they all share a set of standard features....
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Synthetic Biology02:55

Synthetic Biology

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Synthetic biology is an interdisciplinary science that involves using principles from disciplines such as engineering, molecular biology, cell biology, and systems biology. It involves remodeling existing organisms from nature or constructing completely new synthetic organisms for applications such as protein or enzyme production, bioremediation, value-added macromolecule production, and the addition of desirable traits to crops, to name a few.
Golden rice
Golden rice is a genetically modified...
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Applications Of NMR In Biology01:25

Applications Of NMR In Biology

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Nuclear magnetic resonance (NMR) spectroscopy is a very valuable analytical technique for researchers. It has been used for more than 50 years as an analytical tool. F. Bloch and E. Purcell formulated NMR in 1946 and won the 1952 Nobel Prize in Physics  for their work. Biological macromolecules such as proteins, nucleic acids, lipids, and organic molecules including pharmaceutical compounds, can be studied using this versatile tool that exploits the magnetic properties of certain nuclei.
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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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相关实验视频

Updated: Jun 27, 2025

Investigating Protein Sequence-structure-dynamics Relationships with Bio3D-web
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Investigating Protein Sequence-structure-dynamics Relationships with Bio3D-web

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结构生物信息学的深度学习:当前的应用和未来的前景.

Niranjan Kumar1, Rakesh Srivastava2

  • 1School of Computational and Integrative Sciences, Jawaharlal Nehru University, New Delhi, India.

Briefings in bioinformatics
|May 3, 2024
PubMed
概括
此摘要是机器生成的。

深度学习 (DL) 正在通过分析生物学和医疗保健中的大数据来彻底改变结构生物信息学. 本综述详细介绍了从基础到高级的神经网络的DL应用,用于理解生物分子结构.

关键词:
大数据就是大数据.计算机化药物发现.深度学习是一种深度学习.神经网络的神经网络的神经网络结构生物信息学

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A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
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科学领域:

  • 生物信息学是一种生物信息学.
  • 计算生物学 计算生物学
  • 人工智能的人工智能

背景情况:

  • 生物数据的指数增长需要先进的计算方法.
  • 深度学习 (DL) 为分析复杂的生物信息提供了强大的工具.
  • 结构生物信息学是受益于这些进展的关键领域.

研究的目的:

  • 审查深度学习对结构生物信息学的影响.
  • 提供关于DL在分析生物分子结构中的应用的见解.
  • 讨论DL在这个领域的挑战和未来方向.

主要方法:

  • 对结构生物信息学深度学习现有文献的审查.
  • 解释各种深度学习模型 (例如神经网络,卷积网络,变压器网络).
  • 展示DL在理解蛋白质结构和其他生物分子数据中的应用.

主要成果:

  • 深度学习正在改变结构生物信息学,由大数据和计算资源驱动.
  • 包括先进神经网络在内的DL模型在分析生物分子结构方面取得了重大成功.
  • DL正在成为医疗保健和生物数据分析的组成部分.

结论:

  • 深度学习是结构生物信息学正在进行的革命中的一个关键技术.
  • 持续开发DL模型将进一步提高我们对生物分子结构的理解.
  • 整合DL有望彻底改变生物学和医学中的分析过程.