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相关概念视频

Protein-protein Interfaces02:04

Protein-protein Interfaces

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Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a...
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Protein Networks02:26

Protein Networks

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An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
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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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Experimental RNAi02:15

Experimental RNAi

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RNA interference (RNAi) is a cellular mechanism that inhibits gene expression by suppressing its transcription or activating the RNA degradation process. The mechanism was discovered by Andrew Fire and Craig Mello in 1998 in plants. Today, it is observed in almost all eukaryotes, including protozoa, flies, nematodes, insects, parasites, and mammals. This precise cellular mechanism of gene silencing has been developed into a technique that provides an efficient way to identify and determine the...
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RNA Interference01:23

RNA Interference

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RNA interference (RNAi) is a process in which a small non-coding RNA molecule blocks the post-transcriptional expression of a gene by binding to its messenger RNA (mRNA) and preventing the protein from being translated.
This process occurs naturally in cells, often through the activity of genomically-encoded microRNAs. Researchers can take advantage of this mechanism by introducing synthetic RNAs to deactivate specific genes for research or therapeutic purposes. For example, RNAi could be used...
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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.
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相关实验视频

Updated: Jul 18, 2025

Identification of RNAs Engaged in Direct RNA-RNA Interaction with a Long Non-Coding RNA
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Identification of RNAs Engaged in Direct RNA-RNA Interaction with a Long Non-Coding RNA

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RPIPCM:基于编码序列特征的深度网络模型,用于预测lncRNA-蛋白相互作用.

Lejun Gong1, Jingmei Chen1, Xiong Cui1

  • 1School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing, 210023, China.

Computers in biology and medicine
|August 26, 2023
PubMed
概括
此摘要是机器生成的。

一种新的深度网络模型,RPIPCM,使用序列特征准确预测长非编码RNA-蛋白相互作用 (LPIs). 它有效地解决了数据不平衡,为生物医学研究提供了强大的工具.

关键词:
深度网络是一个深度网络.功能编码功能编码.序列 序列是指一个序列.在cRNA-蛋白质相互作用中.

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Sample Preparation for Mass Spectrometry-based Identification of RNA-binding Regions

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In Silico Identification and Characterization of circRNAs During Host-Pathogen Interactions

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Sample Preparation for Mass Spectrometry-based Identification of RNA-binding Regions
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科学领域:

  • 生物信息学是一种生物信息学.
  • 分子生物学分子生物学
  • 计算生物学 计算生物学

背景情况:

  • 长非编码RNA-蛋白相互作用 (LPIs) 对生物调节至关重要.
  • 由于数据不平衡和需要复杂的特征提取,预测LPIs具有挑战性.

研究的目的:

  • 开发一种新的深度网络模型,RPIPCM,用于预测LPIs.
  • 解决LPI预测中不平衡的正负样本的问题.
  • 为了评估序列特征编码对LPI预测的有效性.

主要方法:

  • 利用了深度网络模型 (RPIPCM),结合了对RNA和蛋白质的序列特征编码.
  • 实施了负采样滑窗方法来处理数据不平衡.
  • 在不同的数据集 (RPI488,ATH948,ZEA22133) 上验证了模型,并将性能与现有方法进行了比较.

主要成果:

  • 在多个数据集中,RPIPCM在准确性,回忆,精度,特异性和MCC方面取得了显著的改善.
  • 序列特征编码的性能优于直接原始序列编码,显示出更大的预测能力.
  • 对比实验证实了RPIPCM的有效性和稳定性,特别是在解决数据不平衡方面.

结论:

  • 在没有外部知识的情况下,RPIPCM通过自动挖掘序列特征来有效地预测LPIs.
  • 该模型的低成本和高效率使其成为生物医学研究人员的宝贵工具.
  • 拟议的负采样方法是解决LPI研究数据不平衡的可行解决方案.