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相关概念视频

What is Gene Expression?01:42

What is Gene Expression?

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Overview
Gene expression is the process in which DNA directs the synthesis of functional products, that is, proteins. Cells can regulate gene expression at various stages. It allows organisms to generate different cell types and enables cells to adapt to internal and external factors.
Genetic Information Flows from DNA to RNA to Protein
A gene is a stretch of DNA that serves as the blueprint for functional RNAs and proteins. Since DNA is made up of nucleotides and proteins consist of amino...
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Regulation of Expression at Multiple Steps01:23

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The gene expression in cells is regulated at different stages: (i) transcription, (ii) RNA processing, (iii) RNA localization, and (iv) translation. Transcriptional regulation is mediated by regulatory proteins such as transcription factors, activators, or repressors—these control gene expression by initiating or inhibiting the transcription of genes. Once a precursor or pre-mRNA is produced, it undergoes post-transcriptional modification, including 5' capping, splicing, and the...
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Signaling cascades usually lack linearity. Multiple pathways interact and regulate one another, allowing cells to integrate and respond to diverse environmental stimuli.
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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
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Cell Specific Gene Expression01:58

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相关实验视频

Updated: Jul 12, 2025

A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information
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学习生物可解释的基因表达数据的潜伏表示:路径活动得分学习算法.

Ioulia Karagiannaki1, Krystallia Gourlia2, Vincenzo Lagani3,4

  • 1Institute of Electronic Structure and Laser, Foundation for Research and Technology-Hellas (IESL-FORTH), Heraklion, Greece.

Machine learning
|October 30, 2023
PubMed
概括
此摘要是机器生成的。

我们开发了Pathway Activity Score Learning (PASL),一种用于减少基因表达数据维度的新方法. PASL创建可解释的路径活动得分,在预测准确度方面超过现有方法.

关键词:
不同激活分析的差异性激活分析.缩小尺寸的缩小方式疾病的分类疾病的分类.路径活动活动路径活动.

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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
  • 系统生物学 系统生物学

背景情况:

  • 高维分子基因表达数据集对分析提出了挑战.
  • 现有的缩小维度的方法往往缺乏直接的生物学解释性.
  • 在复杂的数据集中识别有意义的生物模式至关重要.

研究的目的:

  • 引入路径活动得分学习 (PASL),一种新的维度减小算法.
  • 开发一种产生可解释的特征,代表路径活动的方法.
  • 为了提高基因表达数据的预测性能和生物洞察力.

主要方法:

  • 开发了路径活动分数学习 (PASL) 算法来减少维度.
  • 构建的特征可以直接解释为路径活动得分.
  • 在一个大群体 (5万个样本) 上训练PASL,以创建一个通用的基因表达特征字典.
  • 在35,643个持有样本上验证了字典,并将其应用于165个不同的疾病数据集.
  • 使用AutoML工具JADBio来评估信息保留后的转换.

主要成果:

  • 与最先进的方法 (PLIER) 相比,PASL在乳腺癌和白血病数据集上的预测性能优越.
  • 通过PASL构建的通用功能词典被验证为重建准确性.
  • 预测信息在各种疾病中保留在PASL转换的特征空间中.
  • 帕斯尔的潜伏空间提供了直接的生物学解释,与PCA等传统方法不同.

结论:

  • PASL提供了一种有效和可解释的方法来减少基因表达数据的维度.
  • 该算法增强了复杂分子数据的预测建模和生物理解.
  • 通过PASL,可以轻松创建适用于各种生物环境的通用特征表示.