通过使用omics数据和预测建模,对与衰老相关的干预措施进行in-silico评估
Georg Fuellen1, Daniel Palmer2, Claudia Fruijtier3
1Institute for Biostatistics and Informatics in Medicine and Ageing Research, Rostock University Medical Center, Rostock, Germany; UCD Conway Institute of Biomolecular and Biomedical Research, School of Medicine, University College Dublin, Dublin, Ireland.
Ageing research reviews
|May 25, 2025
概括
识别有效的衰老干预措施需要强大的方法. 本研究审查了in-silico方法,优先考虑干预数据,以准确预测寿命延长和安全性评估.
科学领域:
- 衰老的研究研究.
- 计算生物学是一种计算生物学.
- 发现生物标志物的发现.
背景情况:
- 老龄化干预在长期临床评估中面临挑战.
- 使用基于omics的生物标志物的in-silico方法对于预测干预疗效至关重要.
- 目前的预测模型通常在观测数据上训练,可能会产生偏差的结果.
研究的目的:
- 通过计算方法审查和优先考虑评估衰老干预措施的方法.
- 强调使用干预数据,安全评估和标准化基准的重要性.
- 讨论各种特征处理和预测建模技术,包括AI.
主要方法:
- 对老龄化干预措施的现有评估方法的审查.
- 优先训练干预数据上的预测模型,而不是观察数据.
- 探索特征处理,线性/非线性建模,自动化机器学习和人工智能.
主要成果:
- 在干预数据上培训的第一个寿命延长分类器的出现.
- 在预测建模中强调安全性和毒性评估的必要性.
- 讨论用于可靠评估的标准化基准.
结论:
- 可解释和可重现的策略对于计算衰老研究至关重要.
- 整合安全指标和对干预基准的验证对于准确的预测器开发至关重要.
- 未来的方向强调对人工智能驱动的衰老干预预测进行强有力的验证.
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