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相关概念视频

RNA Structure01:23

RNA Structure

70.8K
Overview
The basic structure of RNA consists of a five-carbon sugar and one of four nitrogenous bases. Although most RNA is single-stranded, it can form complex secondary and tertiary structures. Such structures play essential roles in the regulation of transcription and translation.
Different Types of RNA Have the Same Basic Structure
There are three main types of ribonucleic acid (RNA): messenger RNA (mRNA), transfer RNA (tRNA), and ribosomal RNA (rRNA). All three RNA types consist of a...
70.8K
Nucleic Acid Structure01:25

Nucleic Acid Structure

5.9K
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...
5.9K
Types of RNA01:23

Types of RNA

63.0K
Overview
Three main types of RNA are involved in protein synthesis: messenger RNA (mRNA), transfer RNA (tRNA), and ribosomal RNA (rRNA). These RNAs perform diverse functions and can be broadly classified as protein-coding or non-coding RNA. Non-coding RNAs play important roles in the regulation of gene expression in response to developmental and environmental changes. Non-coding RNAs in prokaryotes can be manipulated to develop more effective antibacterial drugs for human or animal use.
RNA...
63.0K
Ribosome Profiling02:24

Ribosome Profiling

3.4K
Ribosome profiling or ribo-sequencing is a deep sequencing technique that produces a snapshot of active translation in a cell. It selectively sequences the mRNAs protected by ribosomes to get an insight into a cell’s translation landscape at any given point in time.
Applications of ribosome profiling
Ribosome profiling has many applications, including in vivo monitoring of translation inside a particular organ or tissue type and quantifying new protein synthesis levels.
The technique...
3.4K
RNA Stability01:53

RNA Stability

33.2K
Intact DNA strands can be found in fossils, while scientists sometimes struggle to keep RNA intact under laboratory conditions. The structural variations between RNA and DNA underlie the differences in their stability and longevity. Because DNA is double-stranded, it is inherently more stable. The single-stranded structure of RNA is less stable but also more flexible and can form weak internal bonds. Additionally, most RNAs in the cell are relatively short, while DNA can be up to 250 million...
33.2K
RNA-seq03:21

RNA-seq

9.8K
RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...
9.8K

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相关实验视频

Updated: May 26, 2025

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

Published on: September 25, 2021

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使用深度学习进行RNA结构预测 - - 一个全面的审查.

Mayank Chaturvedi1, Mahmood A Rashid1, Kuldip K Paliwal1

  • 1Signal Processing Laboratory, School of Engineering and Built Environment, Griffith University, Brisbane, QLD, 4111, Australia.

Computers in biology and medicine
|February 21, 2025
PubMed
概括
此摘要是机器生成的。

深度学习方法显著提高RNA二级结构预测的准确性,帮助RNA功能研究和药物设计. 本综述涵盖特征提取,模型架构和预测方法,确定未来的研究方向.

关键词:
深度学习是一种深度学习.功能提取 功能提取机器学习是机器学习.神经网络的神经网络的神经网络预测RNA的二次结构变压器 变压器 变压器

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Deep Neural Networks for Image-Based Dietary Assessment
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Deep Neural Networks for Image-Based Dietary Assessment

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相关实验视频

Last Updated: May 26, 2025

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
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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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Deep Neural Networks for Image-Based Dietary Assessment

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科学领域:

  • 计算生物学 计算生物学
  • 生物信息学是一种生物信息学.
  • 生命科学中的人工智能

背景情况:

  • 准确的RNA结构预测对于理解RNA功能和开发基于RNA的疗法至关重要.
  • 深度学习已经彻底改变了RNA结构预测,在准确性方面取得了实质性的收益.

研究的目的:

  • 为RNA二次结构预测提供深度学习策略的全面审查.
  • 分类和分析特征提取方法,模型架构和预测方法.
  • 确定该领域的研究差距,挑战和未来方向.

主要方法:

  • 对于RNA结构预测的深度学习现有文献的审查和综合.
  • 将方法分类为特征提取,模型架构和预测策略.
  • 对不同技术和模型进行比较分析,突出优缺点.

主要成果:

  • 深度学习模型在RNA二级结构预测准确度方面取得了显著的改进.
  • 使用各种特征提取技术和模型架构,每个都有特定的优势.
  • 对比分析揭示了当前最先进的方法的性能格局.

结论:

  • 深度学习是推动RNA结构预测的强大工具.
  • 需要进一步的研究来应对当前的挑战,并提高模型的性能和适用性.
  • 本综述为RNA生物学和人工智能交叉点的未来进展提供了见解.