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相关概念视频

lncRNA - Long Non-coding RNAs02:39

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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 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.
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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.
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相关实验视频

Updated: Jun 4, 2025

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
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使用多图对比学习预测非编码RNA和疾病关联.

Si-Lin Sun1,2, Yue-Yi Jiang1,2, Jun-Ping Yang1,2

  • 1College of Information Science Technology, Hainan Normal University, Haikou, 571158, China.

Scientific reports
|January 2, 2025
PubMed
概括
此摘要是机器生成的。

这项研究介绍了K-MGCMLD,这是一种新的深度学习方法,用于预测非编码RNA (miRNA, lncRNA) 和疾病之间的关联. 它在识别这些关键的生物关系方面取得了很高的准确性,从而改善了诊断.

关键词:
疾病 疾病 疾病图表对比学习学习的图表.不同质的图形是不同的图形.这就是米RNAs.多关联预测的预测.在 lncRNAs 中.

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科学领域:

  • 计算生物学是一种计算生物学.
  • 生物信息学是一种生物信息学.
  • 医学中的人工智能

背景情况:

  • 非编码RNAs,包括微RNAs (miRNAs) 和长非编码RNAs (lncRNAs),在生物过程中发挥关键作用.
  • 准确预测非编码RNA与疾病的关联对于早期疾病诊断和了解疾病机制至关重要.
  • 现有的深度学习方法往往具有较低的预测准确性,并且仅限于单个RNA类型疾病关联.

研究的目的:

  • 开发一种先进的深度学习模型,K-Means和多图对比学习,用于预测miRNA,lncRNA和疾病之间的关联 (K-MGCMLD).
  • 通过提高预测准确度,克服现有方法的局限性,并使多个非编码RNA与疾病相关性的同时预测成为可能.

主要方法:

  • 构建了一个整合miRNAs,lncRNAs和疾病的异质图.
  • 雇佣的K-意味着对下方样本进行集群,以平衡正负样本.
  • 利用图形卷积网络 (GCN) 编码器和多图形对比学习来提取特征并捕获拓特征.
  • 应用了XGBoost分类器,用于使用重建的特征进行多关联分类预测.

主要成果:

  • 达到高的曲线下面面积 (AUC) 值:miRNA疾病为0.9542,lncRNA疾病为0.9603,lncRNA-miRNA关联为0.9687.
  • 案例分析验证了肺癌和阿尔茨海默氏症前30个预测的miRNA关联,证明了其实用性.

结论:

  • K-MGCMLD有效地预测了高准确度的多个非编码RNA疾病关联.
  • 提出的方法为疾病关联预测的计算方法提供了显著的进步.
  • 经过验证的预测突出了K-MGCMLD在临床应用中识别疾病相关的非编码RNA方面的潜力.