转换个性化体重预测:从个性化代谢化身到通用化代谢化身
A Abeltino1, C Serantoni1, A Riente1
1Metabolic Intelligence Lab, Neuroscience, Università Cattolica del Sacro Cuore, Largo Francesco Vito 1, 00168, Rome, Italy; Physics for life Science, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy.
Computers in biology and medicine
|February 20, 2025
概括
一般化代谢阿凡达 (GMA) 使用年龄和性别提供准确的,一般化的代谢预测,减少计算时间并消除对个人数据的需求. 这种可扩展的模型提高了预测分析中的更广泛应用的概括性.
科学领域:
- 代谢建模 代谢建模
- 计算生物学是一种计算生物学.
- 预测分析是一种预测分析.
背景情况:
- 机械代谢模型是复杂的,资源密集的.
- 数据驱动模型提供了一个快速,不断更新的解决方案.
- 上一篇 个性化代谢化身 (PMA) 需要个体特定的数据.
研究的目的:
- 开发一个通用的代谢模型 (GMA) 以更广泛的应用.
- 消除对个人数据测量的需求,同时保持预测准确度.
- 与现有模型相比,提高计算效率.
主要方法:
- 提出了将年龄和性别参数纳入的通用代谢化身 (GMA).
- 在理想和现实条件下评估GMA的预测准确性.
- 对个性化代谢化身 (PMA) 进行了比较评估.
主要成果:
- GMA显示了有希望的准确性 (RMSE: 0.54 ± 0.03 理想; 0.92 ± 0.76 现实世界).
- GMA的准确性与PMA的准确性相当 (GMA RMSE:0.44 ± 0.17与PMA RMSE:0.42 ± 0.04).
- GMA显著减少了计算时间 (0.15 ± 0.11 秒与PMA: 12.0 ± 1.22 秒).
结论:
- GMA为代谢预测提供了一个可扩展的,通用化的模型.
- GMA减少了过,并提高了概括性.
- GMA提高了计算效率,并且在有限的数据中保持了强大的性能.
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