Improved Non-Linear Modeling

剂量反应，酶动力学和其他生物科学数据通常使用非线性最小二乘曲线拟合算法建模。这些方法受到异常值和非高斯误差分布的不利影响。关键参数的置信区间可能会产生误导。该提案描述了鲁棒拟合和自举领域的初步非线性优化技术，这些技术已显示出大幅提高生物建模准确性和可靠性的能力。正在寻求资金，以优化分析算法，并开发必要的软件技术，将这些方法添加到现有的商业软件产品中。另外还寻求资金，以支持利用真实世界数据和蒙特-卡罗模拟进行广泛的分析实验。除了在商业软件中提供创新外，该项目还将使这些方法的同行评审出版物所需的彻底研究和开发成为可能。第二阶段的项目设想，其中包括扩展的强大的拟合创新的第一阶段，以实现真正的最大似然优化自适应，基于使用的非线性优化优值函数，适应实际的实验误差分布。拟议的商业应用：第一阶段的创新将在Prism中实施，Prism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.