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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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Improving Translational Accuracy02:07

Improving Translational Accuracy

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Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
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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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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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Gene Therapy00:59

Gene Therapy

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Gene therapy is a technique where a gene is inserted into a person’s cells to prevent or treat a serious disease. The added gene may be a healthy version of the gene that is mutated in the patient, or it could be a different gene that inactivates or compensates for the patient’s disease-causing gene. For example, in patients with severe combined immunodeficiency (SCID) due to a mutation in the gene for the enzyme adenosine deaminase, a functioning version of the gene can be...
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RNA Editing02:23

RNA Editing

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RNA editing is a post-transcriptional modification where a precursor mRNA (pre-mRNA) nucleotide sequence is changed by base insertion, deletion, or modification. The extent of RNA editing varies from a few hundred bases, in mitochondrial DNA of trypanosomes, to a just single base, in nuclear genes of mammals. Even a single base change in the pre-mRNA can convert a codon for one amino acid into the codon for another amino acid or a stop codon. This type of re-coding can significantly affect the...
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相关实验视频

Updated: Jul 19, 2025

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
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KGETCDA:基于变压器的知识图表编码器的高效表示学习框架,用于预测circRNA-疾病关联.

Jinyang Wu1, Zhiwei Ning1, Yidong Ding1

  • 1School of Automation Science and Engineering, Xi'an Jiaotong University, 710049, Shaanxi, China.

Briefings in bioinformatics
|August 17, 2023
PubMed
概括

这项研究介绍了KGETCDA,这是一种用于识别循环RNA-疾病关联 (CDA) 的新计算方法. KGETCDA利用生物知识图和基于变压器的学习来准确预测疾病联系,优于现有模型.

关键词:
循环RNA与疾病的关联不同质的非编码RNA数据库.知识图表知识图表变压器变压器变压器基于网络的可视化.

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Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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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
  • 基因组学就是基因组学.

背景情况:

  • 循环RNAs (circRNAs) 越来越多地被认为是它们在人类疾病进展中的作用.
  • 有效识别circRNA疾病关联 (CDAs) 对疾病诊断和理解至关重要.
  • 现有的CDA预测计算方法面临着数据稀疏性和捕获复杂,高阶交互的挑战.

研究的目的:

  • 开发一种新的计算方法,KGETCDA,用于准确和高效地预测circRNA疾病关联.
  • 解决现有方法的局限性,特别是处理数据稀疏性和探索高级生物信息.
  • 提供一个用户友好的平台 (HNRBase) 来访问和利用开发的预测工具和相关数据.

主要方法:

  • 通过整合超过10个数据库,构建了一个大型异构的非编码RNA数据集.
  • 构建了一个生物知识图,将circRNA,miRNA,lncRNA和疾病之间的关系结合起来.
  • 雇员 基于变压器的知识表示学习和专注的传播,以实现高质量的嵌入生成.
  • 利用多层感知子来预测基于学习嵌入的CDA匹配得分.

主要成果:

  • 与最先进的模型相比,KGETCDA在预测circRNA疾病关联方面表现显著优越.
  • 该方法有效地捕获了高阶交互信息,克服了数据稀疏性的限制.
  • 为数据可视化,下载和预测开发了一个交互式Web平台HNRBase,提高了可用性.

结论:

  • KGETCDA提供了一种强大而准确的计算方法,用于识别circRNA与疾病的关联.
  • 生物知识图和高级学习技术的整合增强了预测能力.
  • 对于研究人类疾病中circRNA功能的研究人员来说,HNRBase提供了一个宝贵的资源.