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.
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.
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.
更多相关视频
06:32Author Spotlight: Automated Deep Brain Stimulation for Parkinson's Disease - Exploring the Possibilities and Challenges of Home Monitoring
Published on: July 14, 2023
16:02Demonstration of the Sequence Alignment to Predict Across Species Susceptibility Tool for Rapid Assessment of Protein Conservation
Published on: February 10, 2023
相关概念视频
Sanger Sequencing
Evolutionary Relationships through Genome Comparisons
Per-Unit Sequence Models
Zero-sequence currents, which are identical in magnitude and phase, generate a neutral current, resulting in voltage drops across the neutral impedance and the low-voltage winding. If the...
Multi-species Conserved Sequences
Although the genome of each species varies greatly from each other, a few sequences are highly conserved. Such conserved...
Drug Concentration Versus Time Correlation
Two pivotal parameters are the minimum effective concentration (MEC) and the minimum toxic concentration (MTC). The MEC is the...
2D NMR: Heteronuclear Single-Quantum Correlation Spectroscopy (HSQC)
