Nonlinear Dose-Response Modeling of High-Throughput Screening Data Using an Evolutionary Algorithm.

ABSTRACT: Nonlinear dose-response relationships exist extensively in the cellular, biochemical, and physiologic processes that are affected by varying levels of biological, chemical, or radiation stress. Modeling such responses is a crucial component of toxicity testing and chemical screening. Traditional model fitting methods such as nonlinear least squares (NLS) are very sensitive to initial parameter values and often had convergence failure. The use of evolutionary algorithms (EAs) has been proposed to address many of the limitations of traditional approaches, but previous methods have been limited in the types of models they can fit. Therefore, we propose the use of an EA for dose-response modeling for a range of potential response model functional forms. This new method can not only fit the mos

SUBMITTER: Ma J 

PROVIDER: S-EPMC7249578 | biostudies-literature | 2020 Apr-Jun

REPOSITORIES: biostudies-literature