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相关概念视频

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DNA sequencing is a fundamental technique that is routinely used in the biological sciences. This method can be applied to a range of questions at different scales - from the sequencing of a cloned DNA fragment or the study of a mutation in a gene up to whole-genome sequencing. However, despite the widespread use of sequencing today, it was not until 1977 that Fredrick Sanger and his collaborators developed the chain-termination method to decode DNA sequences. It relies on the separation of a...
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Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
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Next-generation sequencing technologies have created large genomic databases of a variety of animals and plants. Ever since the human genome project was completed, scientists studied the genome of primates, mammals, and other phylogenetically distant living beings. Such large-scale  studies have provided new insights into the evolutionary relationship between organisms.
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相关实验视频

Updated: Sep 11, 2025

Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons
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SynchDP:基于相关性的序列对齐算法,用于同步纵向临床数据.

Seokhwan Seong1, Seunghwan Bae1, Jaeyeon Jang1

  • 1Kyungpook National University, School of Computer Science and Engineering, Buk-gu, Daegu, 41566, Daegu, Republic of Korea.

Computers in biology and medicine
|August 18, 2025
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概括
此摘要是机器生成的。

一个名为SynchDP的新算法对准了不规则的临床时间序列数据,以跟踪患者的健康进展并发现生物标志物. 这种方法成功地同步了各种患者数据,改善了疾病进展分析.

关键词:
算法算法是一种算法.欧洲人权理事会 欧洲人权理事会序列对齐方式 序列对齐方式单细胞机是一种单细胞机.同步的同步是同步的同步.时间序列时间序列.

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科学领域:

  • 计算生物学 计算生物学
  • 生物信息学是一种生物信息学.
  • 数据科学数据科学数据科学

背景情况:

  • 临床时间序列数据由于不规则的间隔和不同长度而存在挑战.
  • 通过异步和不规则采样数据,很难比较患者的疾病进展.
  • 这阻碍了对基因表达特征和生物标志物发现的准确分析.

研究的目的:

  • 开发一种用于对齐和同步多个临床时间序列的新算法.
  • 为了从输入序列中重新组装代表性模式.
  • 为了便于对患者疾病轨迹进行可靠的比较.

主要方法:

  • 开发了一种基于相关性的动态编程算法SynchDP.
  • 该算法处理不规则和异步时间序列数据.
  • SynchDP使用合成数据和COVID-19患者严重程度等级进行了验证.

主要成果:

  • 与合成数据的现有方法相比,SynchDP显示出更高的对齐质量.
  • 该算法成功地同步了COVID-19患者数据,识别了健康恶化和恢复模式.
  • 与疾病严重程度进展相关的生物标志物通过单细胞转录组数据有效捕获.

结论:

  • SynchDP提供了一种可靠的方法来对齐复杂的临床时间序列.
  • 该算法有助于了解疾病的进展,并识别相关的生物标志物.
  • 这种方法对个性化医学和生物标志物发现有重大影响.