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相关概念视频

lncRNA - Long Non-coding RNAs02:39

lncRNA - Long Non-coding RNAs

8.6K
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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相关实验视频

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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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使用基于增强的集体学习模型找到潜在的 lncRNA-疾病关联.

Liqian Zhou1, Xinhuai Peng1, Lijun Zeng2

  • 1School of Computer Science, Hunan University of Technology, Zhuzhou, Hunan, China.

Frontiers in genetics
|March 18, 2024
PubMed
概括
此摘要是机器生成的。

一个新的计算框架,LDA-SABC,准确地预测了长时间的非编码RNA与疾病的关联. 这种方法可以识别肺癌的潜在IncRNA生物标志物,有助于疾病的诊断和治疗.

关键词:
在 AdaBoost 中使用 AdaBoost.轻GBMM 轻GBM 轻GBM 轻GBM卷积神经网络是一种卷积神经网络.在cRNA疾病关联中.单一价值分解分解的方法

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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:

  • 长非编码RNAs (lncRNAs) 在癌症发展中至关重要,并作为潜在的预后生物标志物.
  • 识别 lncRNA-疾病关联 (LDA) 对生物标志物发现和治疗策略至关重要.
  • 用于LDA识别的实验方法耗时且昂贵.

研究的目的:

  • 开发一种新的计算框架,LDA-SABC,用于准确的lncRNA疾病关联预测.
  • 利用机器学习进行高效和成本效益的LDA识别.
  • 为了确定肺癌的潜在 lncRNA 生物标志物.

主要方法:

  • 开发了LDA-SABC,这是一个基于增强的框架,将LightGBM和AdaBoost与卷积神经网络集成在一起.
  • 使用单数值分解 (SVD) 来提取 lncRNA-疾病关联特征.
  • 使用五倍交叉验证验证并与现有的LDA推断方法进行比较.

主要成果:

  • 在精度,回忆,准确性,F1分数,AUC和AUPR方面,LDA-SABC在四种经典方法中表现优越.
  • 该框架成功预测了肺癌潜在的lncRNA生物标志物.
  • 分别确定7SK和HULC为非小细胞肺癌 (NSCLC) 和肺腺癌 (LUAD) 的潜在生物标志物.

结论:

  • LDA-SABC提供了一种强大而高效的计算方法,用于预测lncRNA与疾病的关联.
  • 已识别的lncRNAs (7SK,HULC) 需要进一步研究,作为肺癌亚型的潜在生物标志物.
  • 这种方法可以显著帮助推进LDA识别和癌症生物标志物发现.