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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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通过图形表示学习和基因图增强来解码潜在的 lncRNA 和疾病关联

Lili Tang1, Longlong Liu2, Yan Jiang3,4

  • 1School of Computer Science and Artificial Intelligence, Hunan University of Technology, Zhuzhou, 412007, China.

Scientific reports
|August 26, 2025
PubMed
概括

本研究介绍了LDA-GMCB,这是预测长非编码RNA疾病关联 (LDA) 的新型模型. LDA-GMCB显著优于现有方法,提供了更快,更有效的方法来识别与疾病相关的 lncRNA.

关键词:
图形嵌入基于直方图的梯度增强与LncRNA疾病的关联多头自我注意力与CNN

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科学领域:

  • 基因组学
  • 生物信息学
  • 计算生物学

背景情况:

  • 长非编码RNA (lncRNAs) 在复杂疾病中起着至关重要的作用.
  • 对 lncRNA-疾病关联 (LDA) 的实验验证是资源密集的.
  • 准确预测LDA对于疾病的理解和治疗发展至关重要.

研究的目的:

  • 为推断 lncRNA - 疾病关联 (LDA) 开发一种高效准确的计算模型.
  • 利用先进的机器学习技术来提高LDA预测.
  • 为识别复杂疾病的潜在 lncRNA生物标志物提供一个有价值的工具.

主要方法:

  • LDA-GMCB模型集成了图形嵌入式学习,多头自我注意 (MSA) 与卷积神经网络 (CNN),低级单数值分解 (SVD) 和基于直方图的梯度增强 (HGBoost).
  • 使用图形嵌入和MSA-CNN捕获非线性特征,而通过低级SVD提取线性特征.
  • HGBoost用于最终推断 lncRNA与疾病的关系.

主要成果:

  • 与四个基线模型和四个流行的分类器相比,LDA-GMCB在5倍交叉验证和冷启动场景中表现出更好的表现.
  • 该模型在 lncRNADisease 和 MNDR 数据库中取得了显著的改进.
  • 废除研究证实了LDA-GMCB中的各个成分的有效性.

结论:

  • LDA-GMCB为预测 lncRNA-疾病关联提供了强大而高效的计算方法.
  • 该模型成功识别了与阿尔茨海默病和帕金森病相关的潜在 lncRNA (DGCR5,HIF1A).
  • 对于复杂疾病中 lncRNA 功能的未来研究, LDA-GMCB 提供了宝贵的资源.