Determination of Polynomial Degree in the Regression of Drug Combinations

Boqian Wang; Xianting Ding; Fei-Yue Wang

Volume 4 Issue 1

Jan. 2017

IEEE/CAA Journal of Automatica Sinica

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Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2017 > 4(1): 41-47

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.

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.

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Determination of Polynomial Degree in the Regression of Drug Combinations

Boqian Wang^1
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Xianting Ding^{1
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Fei-Yue Wang^{2, 3
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1.
School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China
2.
State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Science (SKL-MCCS, CASIA), Beijing 100190, China
3.
Research Center of Computational Experiments and Parallel Systems, National University of Defense Technology, Changsha 410073, China

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Author Bio:
Boqian Wang graduated from Zhiyuan College, Shanghai Jiao Tong University (SJTU), China, in 2016, with the B.S degree. He is currently a master student at the School of Biomedical Engineering, SJTU, China. His research interests include drug combination optimization and CyTOF data analysis.(e-mail: wangbo-qian 723@163.com)

Fei-Yue Wang received his Ph. D. degree in computer and systems engineering from Rensselaer Polytechnic Institute, Troy, New York in 1990. He joined the University of Arizona in 1990 and became a Professor and Director of the Robotics and Automation Lab (RAL) and Program in Advanced Research for Complex Systems (PARCS). In 1999, he founded the Intelligent Control and Systems Engineering Center at the Institute of Automation, Chinese Academy of Sciences (CAS), Beijing, China, under the support of the Outstanding Oversea Chinese Talents Program from the State Planning Council and "100 Talent Program" from CAS, and in 2002, was appointed as the Director of the Key Laboratory of Complex Systems and Intelligence Science, CAS. In 2011, he became the State Specially Appointed Expert and the Director of the State Key Laboratory of Management and Control for Complex Systems. Dr. Wang's current research focuses on methods and applications for parallel systems, social computing, and knowledge automation. He was the Founding Editorin-Chief of the International Journal of Intelligent Control and Systems (1995-2000), Founding EiC of IEEE ITS Magazine (2006-2007), EiC of IEEE Intelligent Systems (2009-2012), and EiC of IEEE Transactions on ITS (2009-2016). Currently he is EiC of China's Journal of Command and Control. Since 1997, he has served as General or Program Chair of more than 20 IEEE, INFORMS, ACM, ASME conferences. He was the President of IEEE ITS Society (2005-2007), Chinese Association for Science and Technology (CAST, USA) in 2005, the American Zhu Kezhen Education Foundation (2007-2008), and the Vice President of the ACM China Council (2010-2011). Since 2008, he is the Vice President and Secretary General of Chinese Association of Automation. Dr. Wang is elected Fellow of IEEE, INCOSE, IFAC, ASME, and AAAS. In 2007, he received the 2nd Class National Prize in Natural Sciences of China and awarded the Outstanding Scientist by ACM for his work in intelligent control and social computing. He received IEEE ITS Outstanding Application and Research Awards in 2009 and 2011, and IEEE SMC Norbert Wiener Award in 2014.(e-mail:feiyue.wang@ia.ac.cn)
Corresponding author: Xianting Ding is associate professor at the School of Biomedical Engineering, Institute for Personalized Medicine, Shanghai Jiao Tong University. He received his Ph.D. degree from the Department of Mechanical Engineering at University of California, Los Angeles (UCLA) in 2012. His research interests focus on developing personalized therapy and precision medicine, including:1) developing bio-sensors for early detection of cancer, infectious disease, metabolic diseases, age-related diseases, cardiovascular diseases, based on cellular electrochemical, impedance, mechanical and photonic signaling; 2) developing personalized treatment; optimizing drug combinations; studying drug-drug interactions; building up models for bio-complex systems, based on Feedback System Control (FSC), Microfluidics and Bio-MEMS; 3) traditional Chinese medicine (TCM) modernization, extraction, purification, and re-combination for treating ischemia, osteoporosis and depression. He participated in the early investigation and development for 3 international research centers, including Institute for Cell Mimetic Space Exploration (CMISE, funded by NASA), Center for Cell Control (CCC, funded by NIH) and Institute for Personalized Medicine (IPM, funded by Chinese Central Organization Department). He is now leading 16 combinatorialdrug-optimization related international projects. He is the editorial board member for Journal of the Association for Laboratory Automation (JALA), the reviewer for 7 journals, published 26 peer reviewed journal papers and filed 11 patents. Corresponding author of this paper (e-mail:dingxianting@gmail.com)
Received Date: 2016-10-06
Accepted Date: 2016-11-28

Abstract

Abstract

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.
- Cross-validation,
- drug combination,
- polynomial regression,
- polynomial degree,
- overfitting

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References

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