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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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Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

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Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...
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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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相关实验视频

Updated: Jul 9, 2025

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
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基于异质图的卷积生成对抗网络预测 lncRNA-疾病关联.

Zhonghao Lu1, Hua Zhong1, Lin Tang2

  • 1School of Information, Yunnan Normal University, Yunnan, People's Republic of China.

PLoS computational biology
|November 29, 2023
PubMed
概括

本研究介绍了HGC-GAN,这是一种用于预测长非编码RNA (lncRNA) 和疾病关联的新计算方法. HGC-GAN有效地识别了潜在的联系,有助于疾病诊断和药物开发.

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科学领域:

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

背景情况:

  • 长非编码RNA (lncRNAs) 与各种疾病有关,但预测它们与疾病的关联是具有挑战性的.
  • 现有的方法在 lncRNA-疾病关联预测中扎于异质数据和数据不平衡.

研究的目的:

  • 开发一种新的计算方法,HGC-GAN,用于准确预测lncRNA-疾病关联.
  • 解决处理异质信息和稀疏,不平衡数据的局限性.

主要方法:

  • 构建了一个lncRNA-miRNA-疾病异质网络,集成多个关联数据和序列信息.
  • 使用异质图卷积神经网络 (GCN) 作为生成器来获得节点嵌入.
  • 使用生成对抗网络 (GAN) 区分器来完善生成器的预测准确性.

主要成果:

  • 在预测lncRNA与疾病的关联方面,HGC-GAN获得了高性能,AUC为0.9591和AUPR为0.9606.
  • 在预测新 lncRNAs 的关联方面表现出有效性.
  • 实验结果证实了该方法的卓越预测能力.

结论:

  • HGC-GAN提供了一种有前途的计算方法,用于预测lncRNA与疾病的关联.
  • 该方法对疾病诊断,治疗策略和药物开发有潜在的影响.
  • 强调将GCN和GAN集成到复杂的生物网络分析中的实用性.