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相关概念视频

RNA-seq03:21

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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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RACE - Rapid Amplification of cDNA Ends02:35

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Rapid Amplification of cDNA Ends, or RACE, is one of the most effective methods to obtain a full-length cDNA from an mRNA sequence between a known internal region to the unknown sequence at the 5’ or 3’ end. The unknown region is cloned in the cDNA by a gene-specific primer that binds the known end, and a hybrid primer that attaches a predefined anchor sequence to the unknown end of the cDNA. The sequence in between is amplified by PCR with an anchor primer and a gene-specific...
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Rous Sarcoma virus or RSV was discovered by F. Peyton Rous in the year 1911 as a filterable transmissible agent that could cause tumors in chickens. He won a Nobel Prize for this discovery in 1966. His experiments clearly demonstrated that some cancers could be caused by infectious agents and led to the discovery of many more cancer-causing viruses in animals as well as humans.
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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.
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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.
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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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相关实验视频

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RSRNeT:一个新的多模式网络框架,用于命名实体识别和关系提取.

Min Wang1,2, Hongbin Chen1, Dingcai Shen1,2

  • 1School of Mathematics and Computer Science, Gannan Normal University, Ganzhou, China.

PeerJ. Computer science
|December 13, 2024
PubMed
概括

本研究介绍了RSRNeT,这是一个用于命名实体识别 (NER) 和关系提取 (RE) 的新型多模式网络. 通过充分提取视觉特征和全面融合多模式数据,RSRNeT提高了性能,克服了仅文本和现有的多模式方法的局限性.

关键词:
贝尔特 (BERT) 公司多模式特征融合多模式特征融合多模式命名实体识别多模式命名实体识别多模态关系提取多模态关系提取多尺度视觉特征提取 多尺度视觉特征提取

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科学领域:

  • 自然语言处理自然语言处理.
  • 计算机视觉 计算机视觉
  • 人工智能的人工智能

背景情况:

  • 命名实体识别 (NER) 和关系提取 (RE) 对于知识图构建至关重要.
  • 仅依赖文本的单模方法在性能和效率上表现出局限性,特别是在多模单词方面.
  • 当有无关图像存在时,NER (MNER) 和RE (MRE) 现有的多模式方法可能是低效的.

研究的目的:

  • 提出一个新的多模式网络框架,RSRNeT,用于增强命名实体识别和关系提取.
  • 改进视觉特征的提取和综合融合多模式信息.
  • 为了减轻多模式学习任务中无关图像引起的性能下降.

主要方法:

  • 使用ResNeSt网络开发了一个多尺度的视觉特征提取模块.
  • 基于RoBERTa网络设计了一个多模特的功能融合模块.
  • 集成这些模块来学习强大的视觉和文本表示,最大限度地减少不相关的视觉数据的干扰.

主要成果:

  • RSRNeT在MNER和MRE任务上展示了最先进的性能.
  • 与基线模型相比,获得了优异的回忆和F1分数.
  • 在三个公共数据集上验证了有效性:Twitter2015,Twitter2017和MNRE.

结论:

  • 拟议的RSRNeT框架有效地增强了多模式命名实体识别和关系提取.
  • 这些新型模块能够实现卓越的特征提取和融合,从而提高准确性和效率.
  • RSRNeT为从多模式数据中提取知识提供了一个有希望的解决方案,特别是在具有无关紧要信息的具有挑战性的场景中.