对于SDE系数函数的无假设神经推理的损失配方
Marc Vaisband1,2,3, Valentin von Bornhaupt3, Nina Schmid3
1Department of Internal Medicine III with Haematology, Medical Oncology, Haemostaseology, Infectiology and Rheumatology, Oncologic Center, Salzburg Cancer Research Institute - Laboratory for Immunological and Molecular Cancer Research (SCRI-LIMCR), Salzburg, Austria.
NPJ systems biology and applications
|March 2, 2025
概括
本研究引入了一种新的方法来分析通过随机微分方程 (SDEs) 建模的复杂生物过程. 该方法提高了使用神经网络和联合优化目标的动态系统的预测准确性.
科学领域:
- 动态系统和概率理论.
- 计算生物学和生物信息学
- 机器学习用于科学建模
背景情况:
- 随机微分方程 (SDEs) 对于模拟复杂的生物过程至关重要.
- 推断SDEs的现有方法通常依赖神经网络来参数化系数.
- 需要提高预测性能和灵活性来学习各种动态.
研究的目的:
- 为推断SDEs提出一个新的优化目标.
- 为了提高动态系统模型的预测性能.
- 为了使各种动态的学习没有先前的结构假设.
主要方法:
- 使用神经网络进行SDEs的参数化系数函数.
- 开发一个新的优化目标,结合基于模拟的惩罚和伪相似的生活.
- 与最先进的方法对比,评估拟议的方法.
主要成果:
- 与现有方法相比,显著提高了预测性能.
- 成功学习各种复杂的生物动态.
- 证明了基于模拟的联合处罚和伪相似生活条件的有效性.
结论:
- 新型优化目标为生物建模中的SDE推理提供了一种强大的方法.
- 这种方法推进了学习复杂动态系统的最新技术.
- 该方法为分析生物过程提供了一个灵活的框架,没有限制性假设.
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