A journal of IEEE and CAA , publishes high-quality papers in English on original theoretical/experimental research and development in all areas of automation
Volume 4 Issue 1
Jan.  2017

IEEE/CAA Journal of Automatica Sinica

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Article Contents
Boqian Wang, Xianting Ding and Fei-Yue Wang, "Determination of Polynomial Degree in the Regression of Drug Combinations," IEEE/CAA J. Autom. Sinica, vol. 4, no. 1, pp. 41-47, Jan. 2017.
Citation: Boqian Wang, Xianting Ding and Fei-Yue Wang, "Determination of Polynomial Degree in the Regression of Drug Combinations," IEEE/CAA J. Autom. Sinica, vol. 4, no. 1, pp. 41-47, Jan. 2017.

Determination of Polynomial Degree in the Regression of Drug Combinations

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  • Studies on drug combinations are becoming more and more popular in the past few decades, with the development of computer and algorithms. One of the most common methods in optimizing drug combinations is regression of a polynomial model based on certain number of experimental observations. In this paper, we study how to determine the degree of polynomials in different circumstances of drug combination optimization. Using cross-validation, we have found that in most cases, a high degree results in failures of accurate prediction, named overfitting. An anti-noise test has also revealed that polynomial model with high degree tends to be less resistant to random errors in the observations.

     

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