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Updated: Feb 4, 2026

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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Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons

Published on: June 9, 2023

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非线性时间序列的无模型预测

Xiaoyong Wu1,2, Shesh N Rai1,2, Georg F Weber3

  • 1Biostatistics and Informatics Shared Resource, University of Cincinnati Cancer Center, College of Medicine, Cincinnati, Ohio, United States of America.

PloS one
|February 2, 2026
PubMed
概括

预测传染病的传播至关重要. 这项研究表明,基于时间滞后数据的机器学习可以在没有复杂模型的情况下预测疾病的进展,从而提供有价值的短期预测.

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