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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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Regulation of Nuclear Protein Sorting01:45

Regulation of Nuclear Protein Sorting

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Nuclear protein sorting regulates nucleus composition and gene expression, crucial for determining the fate of a eukaryotic cell. Hence, the entry and exit of molecules across the nuclear envelope is a tightly controlled process. Nuclear protein sorting can be inhibited by one of the following ways: 1) masking cargo signal sequences, 2) modifying the nuclear receptor's affinity for cargo, 3) controlling the nuclear pore size, 4) retaining the cargo during its transit to the cytosol or the...
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Nuclear Localization Signals and Import01:46

Nuclear Localization Signals and Import

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Proteins targeted to the nucleus carry short stretches of amino acid sequences called the nuclear localization signal or NLS. Classical nuclear localization signals are of two types: monopartite and bipartite NLS. Monopartite classical NLS (cNLS) consists of a single cluster of 4-8 amino acids. Bipartite cNLS consists of two clusters of  2-3 amino acids and a 9-12 residue long proline-rich linker bridging the two clusters. Signal clusters are rich in positively charged amino acids such as...
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Cancer Survival Analysis01:21

Cancer Survival Analysis

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Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
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Improving Translational Accuracy02:07

Improving Translational Accuracy

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Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
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Multiple Regression01:25

Multiple Regression

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Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
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相关实验视频

Updated: Sep 18, 2025

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
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MLWNNR:LncRNA-疾病关联预测与多核学习驱动的权重核规范规范化.

Guo-Bo Xie1, Hao-Jie Xu1, Guo-Sheng Gu2

  • 1School of Computer Science, Guangdong University of Technology, Guangzhou, 510000, China.

Interdisciplinary sciences, computational life sciences
|June 23, 2025
PubMed
概括

这项研究引入了一种新的算法,MLWNNR,用于预测长非编码RNA (lncRNA) 与疾病之间的联系. 该方法准确地识别了潜在的关联,有助于理解人类的病理.

关键词:
k-最近邻居以核心为中心的内核学习算法多核学习是多核学习.权重核规范规范化规范化疾病 疾病 疾病 疾病在cnRNA中.

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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.
  • 计算生物学 计算生物学

背景情况:

  • 长非编码RNAs (lncRNAs) 是人类疾病的关键调节者.
  • 预测lncRNA与疾病的关联对于理解疾病机制至关重要.

研究的目的:

  • 开发一个强大的算法来预测 lncRNA-疾病的关联.
  • 为了利用多核学习和网络完成,进行准确的预测.

主要方法:

  • 利用基于k-最近邻近的内核学习算法来整合多相似性内核.
  • 构建了一个异质的 lncRNA-疾病关联网络.
  • 应用加权核规范规范化用于网络完成和协会评分.

主要成果:

  • 与其他六种模型相比,MLWNNR算法在三个数据集上表现出卓越的性能.
  • 案例研究证实了大多数预测的lncRNA疾病关联与现有文献.
  • 该模型表现出强性和出色的概括能力.

结论:

  • MLWNNR提供了一种可靠的计算方法来推断lncRNA与疾病的关联.
  • 这种方法可以帮助识别人类疾病的新生物标志物和治疗点.