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相关概念视频

RNA-seq03:21

RNA-seq

9.9K
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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Ribosome Profiling02:24

Ribosome Profiling

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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...
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相关实验视频

Updated: Jun 24, 2025

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
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Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

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机器学习和转录学中的相关方法.

Yuning Cheng1, Si-Mei Xu1, Kristina Santucci1

  • 1School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, NSW, 2052, Australia.

Biochemical and biophysical research communications
|June 9, 2024
PubMed
概括
此摘要是机器生成的。

机器学习 (ML) 正在通过有效处理复杂数据来彻底改变人类转录组分析. 本综述指导生物信息学家通过ML技术及其在转录组学研究中的应用.

关键词:
深度学习是一种深度学习.疾病预测 疾病预测表观遗传修饰 表观遗传修饰 表观遗传修饰长读序列的测序方式机器学习是机器学习.微阵列的微阵列有关RNA测序的RNA测序单细胞转录组学 单细胞转录组学文字转录学 (Transcriptomics) 是一个学科.

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Author Spotlight: Cost-Effective Transcriptomic Drug Screening - Unlocking New Targets

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Author Spotlight: Cost-Effective Transcriptomic Drug Screening - Unlocking New Targets
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科学领域:

  • 生物信息学是一种生物信息学.
  • 基因组学就是基因组学.
  • 计算生物学 计算生物学

背景情况:

  • 转录组数据生成不再是分析管道的主要瓶.
  • 转录组分析的进步已经将重点转移到复杂的数据分析方法.
  • 机器学习 (ML) 为分析大型和复杂的人类转录组数据集提供了高效的解决方案.

研究的目的:

  • 弥合生物信息学家在人类转录学中的ML知识差距.
  • 提供ML类型和适用于转录组分析的技术的全面概述.
  • 突出最近 (过去五年) 在人类转录组调查中ML的应用.

主要方法:

  • 审查最近的科学文献 (主要是过去五年) 在人类转录学中的ML.
  • 一般和特定的ML技术的分类.
  • 对计算方面的分析,包括数据预处理,任务制定,模型性能和验证.

主要成果:

  • 在人体转录组分析管道中集成的ML技术的演示.
  • 基于ML的结果与传统的非ML分析工具的比较.
  • 确定在转录学学中实施ML的关键计算考虑因素.

结论:

  • ML提供了一种强大的方法来增强对人类转录组数据的分析.
  • 对生物信息学家来说,了解ML方法对于利用这些先进工具至关重要.
  • 本综述是将ML应用于人类转录基因研究的实用指南.