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相关概念视频

lncRNA - Long Non-coding RNAs02:39

lncRNA - Long Non-coding RNAs

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In humans, more than 80% of the genome gets transcribed. However, only around 2% of the genome codes for proteins. The remaining part produces non-coding RNAs which includes ribosomal RNAs, transfer RNAs, telomerase RNAs, and regulatory RNAs, among other types. A large number of regulatory non-coding RNAs have been classified into two groups depending upon their length – small non-coding RNAs, such as microRNA, which are less than 200 nucleotides in length, and long non-coding RNA...
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RNA-seq03:21

RNA-seq

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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...
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相关实验视频

Updated: Jun 13, 2025

Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2
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Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2

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对于非编码RNA分类的深度学习方法的比较和基准.

Constance Creux1,2, Farida Zehraoui1, François Radvanyi2

  • 1Université Paris-Saclay, Univ Evry, IBISC, Evry-Courcouronnes, France.

PLoS computational biology
|September 12, 2024
PubMed
概括
此摘要是机器生成的。

本研究比较了用于分类非编码RNA (ncRNA) 的深度学习方法. 它为未来的ncRNA功能分类工具提供了基准和建议.

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Identification of Key Factors Regulating Self-renewal and Differentiation in EML Hematopoietic Precursor Cells by RNA-sequencing Analysis
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Identification of Key Factors Regulating Self-renewal and Differentiation in EML Hematopoietic Precursor Cells by RNA-sequencing Analysis

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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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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:

  • 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:

  • 非编码RNAs (ncRNAs) 在生物过程和疾病中起着至关重要的作用.
  • 了解ncRNA的功能至关重要,但大多数ncRNA仍然没有表征.
  • 对于大量的ncRNAs,需要快速分类方法.

研究的目的:

  • 为ncRNA分类提供最先进的深度学习方法的详尽比较.
  • 为评估ncRNA分类工具建立客观的基准.
  • 评估建筑选择对性能,强度和计算效率的影响.

主要方法:

  • 系统审查和对现有的深度学习架构进行ncRNA分类的比较.
  • 在流行的数据集上对各种工具的实验性评估.
  • 对非功能序列和噪声的方法稳定性的分析.
  • 测量计算时间和二氧化碳排放.

主要成果:

  • 深度学习方法对ncRNA分类有希望.
  • 在不同的架构和数据集中,性能差异很大.
  • 对噪声的稳定性和计算效率是关键考虑因素.
  • 特定的建筑选择会影响性能和资源利用.

结论:

  • 为未来的ncRNA分类方法开发提供了建议.
  • 客观的基准对于公平的工具评估至关重要.
  • 进一步的研究应侧重于提高稳定性和效率,同时考虑环境影响.