Improved Non-Linear Modeling

生物科学中的剂量响应、酶动力学和其他数据通常使用非线性最小二乘曲线拟合算法来建模。这些方法都受到离群值和非高斯误差分布的不利影响。在关键参数上可能会产生误导的可信区间。这项建议描述了在稳健拟合和自举领域的初步非线性优化技术，这些技术已经显示出显著提高生物建模的准确性和可靠性的能力。寻求资金用于优化分析算法和开发必要的软件技术，以便将这些方法添加到已建立的商业软件产品中。还寻求资金支持使用真实世界数据和蒙特卡洛模拟进行的广泛分析实验。除了在商业软件中提供创新之外，该项目还将使对这些方法的同行审查出版所需的彻底研究和开发成为可能。第二阶段项目包括扩展第一阶段的稳健拟合创新，以基于适应于实际实验误差分布的非线性优化价值函数，自适应地实现真正的最大似然优化。拟议的商业应用：第一阶段的创新将在棱镜中实施，棱镜是世界领先的生物建模商业软件应用。

Dose-response, enzyme kinetics, and other data in the biosciences are often modeled using non-linear least-squares curve-fitting algorithms. These methods are adversely impacted both by outliers and a non- Gaussian error distribution. Misleading confidence intervals on crucial parameters can result. This proposal describes preliminary non-linear optimization technology in the areas of robust fitting and bootstrapping that have shown the ability to substantially improve the accuracy and reliability of biomodeling. Funding is sought for the optimization of the analytic algorithms and the development of the software technology necessary to add these methods to an established commercial software product. Funding is also sought for the support of extensive analysis experiments with both real-world data and Monte-Carlo simulations. In addition to making the innovation available in commercial software, this project will enable the thorough research and development necessary for peer-reviewed publication of these methods. A Phase II project is envisioned which includes extending the robust fitting innovations of Phase I in order to achieve the true maximum-likelihood optimization adaptively, based on the use of a non-linear optimization merit function that adapts to actual experimental error distribution. PROPOSED COMMERCIAL APPLICATIONS: The Phase I innovations will be implemented in Prism, the world's leading commercial software application for biomodeling.