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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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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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Regulated mRNA Transport02:22

Regulated mRNA Transport

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In eukaryotes, transcription and translation are compartmentalized; an mRNA is first synthesized in the nucleus and then selectively transported to the cytoplasm for protein synthesis. Before transport, a pre-mRNA undergoes several steps of post-transcriptional modifications including splicing, 5' capping, and the addition of a poly-adenine tail. Various proteins bind to the pre-mRNA during these modifications. The mRNA transport takes place with the help of multiple proteins playing...
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DlncRNALoc:一种基于离散波纹转换的模型,用于预测 lncRNA 亚细胞局部化.

Xiangzheng Fu1,2,3, Yifan Chen2,3, Sha Tian4

  • 1Neher's Biophysics Laboratory for Innovative Drug Discovery, State Key Laboratory of Quality Research in Chinese Medicine, Macau Institute for Applied Research in Medicine and Health, Macau University of Science and Technology, Macao, China.

Mathematical biosciences and engineering : MBE
|December 21, 2023
PubMed
概括

预测长非编码RNA (lncRNA) 细胞下定位对于理解细胞功能至关重要. 一个新的计算模型,DlncRNALoc,利用离散波量变换来准确地预测lncRNA本地化.

关键词:
离散的波形变换.lncRNA亚细胞局部化当地渔民歧视性分析物理化学性质矩阵.合成少数人过量采样

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科学领域:

  • 计算生物学 计算生物学
  • 分子生物学分子生物学
  • 生物信息学是一种生物信息学.

背景情况:

  • 预测长非编码RNA (lncRNA) 细胞下定位对于理解基因调节和细胞功能至关重要.
  • 对于 lncRNA 定位的实验方法是艰苦而昂贵的,需要高效的计算方法.
  • 由于 lncRNA 的结构复杂性和不平衡的数据分布,现有的计算方法面临着挑战.

研究的目的:

  • 开发一个准确和有效的计算模型来预测lncRNA亚细胞局部化 (LSL).
  • 通过结合先进的特征提取和优化技术来解决当前方法的局限性.

主要方法:

  • 一种基于离散波形变换 (DWT) 的新特征提取方法,用于lncRNA序列.
  • 基于双基的物理化学性质矩阵的构造.
  • 合成少数人过量采样技术 (SMOTE) 的应用,用于数据平衡.
  • 使用当地渔民区分分析 (LFDA) 优化特征信息.
  • 使用支持矢量机器 (SVM) 开发一个预测模型.

主要成果:

  • DlncRNALoc模型在预测LSL方面表现出卓越的性能.
  • 广泛的交叉验证实验证实了该模型在基准数据集上的有效性.
  • 拟议的基于DWT的特征提取和优化显著提高了预测准确性.

结论:

  • DlncRNALoc为 lncRNA 亚细胞局部化预测提供了一个强大而高效的计算解决方案.
  • 整合DWT,SMOTE和LFDA提供了一个强大的框架来分析lncRNA序列数据.
  • 这种模型通过精确的定位改进了 lncRNA 功能的预测,从而推动了生物信息学领域的发展.