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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

12.2K
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...
12.2K

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相关实验视频

Updated: May 17, 2025

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
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Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

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邻近规范矩阵因子化为lncRNA-疾病关联识别的因子化.

Jihwan Ha1, Kwangsu Kim2

  • 1Major of Big Data Convergence, Division of Data Information Science, Pukyong National University, Busan 48513, Republic of Korea.

International journal of molecular sciences
|May 14, 2025
PubMed
概括

这项研究介绍了NRMFLDA,这是一种用于预测与疾病相关的长非编码RNA (lncRNAs) 的新型模型. 该模型准确地识别了与疾病相关的 lncRNA,有助于生物标志物发现和推进诊断.

关键词:
疾病 疾病 疾病 疾病在 lncRNA 中,在cRNA疾病关联中.机器学习是机器学习.矩阵分解因子化

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科学领域:

  • 基因组学就是基因组学.
  • 生物信息学是一种生物信息学.
  • 分子生物学分子生物学

背景情况:

  • 长非编码RNA (lncRNAs) 在生物过程和人类疾病中起着关键作用.
  • 识别 lncRNA-疾病关联对于疾病生物标志物发现和治疗开发至关重要.

研究的目的:

  • 开发一种有效的计算模型来推断与疾病相关的 lncRNAs.
  • 为了提高 lncRNA-疾病关联预测的准确性和可靠性.

主要方法:

  • 提出了一个基于推系统的模型,命名为NRMFLDA.
  • 使用矩阵因子化与疾病社区规范化.
  • 员工一次性休假和绩效评估的五倍交叉验证.

主要成果:

  • NRMFLDA实现了高性能,其AUC分数为0.9143和0.8993. 这两种分数均为0.9143和0.8993.
  • 在预测 lncRNA - 疾病关联方面表现优于以前建立的四种模型.
  • 在识别与疾病相关的 lncRNA 方面表现出强度和有效性.

结论:

  • NRMFLDA 提供了一种创新的方法来发现 lncRNA 与疾病的关联.
  • 该模型可以为识别新型疾病生物标志物做出重大贡献.
  • 通过这项研究,预计诊断和治疗策略将取得进展.