Department of Statistics, North Carolina State University, Raleigh, North Carolina; CellzDirect/Invitrogen Corporation (a part of Life Technologies), Durham, North Carolina.
Dose Response. 2011;9(3):387-409. doi: 10.2203/dose-response.09-030.Beam. Epub 2010 Jun 25.
An essential part of toxicity and chemical screening is assessing the concentrated related effects of a test article. Most often this concentration-response is a nonlinear, necessitating sophisticated regression methodologies. The parameters derived from curve fitting are essential in determining a test article's potency (EC(50)) and efficacy (E(max)) and variations in model fit may lead to different conclusions about an article's performance and safety. Previous approaches have leveraged advanced statistical and mathematical techniques to implement nonlinear least squares (NLS) for obtaining the parameters defining such a curve. These approaches, while mathematically rigorous, suffer from initial value sensitivity, computational intensity, and rely on complex and intricate computational and numerical techniques. However if there is a known mathematical model that can reliably predict the data, then nonlinear regression may be equally viewed as parameter optimization. In this context, one may utilize proven techniques from machine learning, such as evolutionary algorithms, which are robust, powerful, and require far less computational framework to optimize the defining parameters. In the current study we present a new method that uses such techniques, Evolutionary Algorithm Dose Response Modeling (EADRM), and demonstrate its effectiveness compared to more conventional methods on both real and simulated data.
毒性和化学筛选的一个重要部分是评估测试物质的浓缩相关效应。通常，这种浓度-反应是非线性的，需要复杂的回归方法。从曲线拟合中得出的参数对于确定测试物质的效力（EC（50））和功效（E（max））至关重要，模型拟合的差异可能导致对物质性能和安全性的不同结论。以前的方法利用先进的统计和数学技术来实施非线性最小二乘法（NLS）以获得定义此类曲线的参数。这些方法虽然在数学上严格，但存在初始值敏感性、计算强度问题，并且依赖于复杂而精细的计算和数值技术。但是，如果存在可以可靠地预测数据的已知数学模型，则非线性回归也可以被视为参数优化。在这种情况下，可以利用机器学习中的经过验证的技术，例如进化算法，这些技术具有强大的鲁棒性，功能强大，并且需要更少的计算框架来优化定义参数。在当前的研究中，我们提出了一种新方法，该方法使用这种技术，即进化算法剂量反应建模（EADRM），并在真实和模拟数据上与更传统的方法进行了比较，展示了其有效性。
