Constrained Gaussian process with application in tissue-engineering scaffold biodegradation

ABSTRACT

In many biomanufacturing areas, such as tissue-engineering scaffold fabrication, the biodegradation performance of products is a key to producing products with desirable properties. The prediction of biodegradation often encounters the challenge of how to incorporate expert knowledge. This article proposes a Constrained Gaussian Process (CGP) method for predictive modeling with application to scaffold biodegradation. It provides a unified framework of using appropriate constraints to accommodate various types of expert knowledge in predictive modeling, including censoring, monotonicity, and bounds requirements. Efficient Bayesian sampling procedures for prediction are also developed. The performance of the proposed method is demonstrated in a case study on a novel scaffold fabrication process. Compared with the unconstrained GP and artificial neural networks, the proposed method can provide more accurate and meaningful prediction. A simulation study is also conducted to further reveal the properties of the CGP.

KEYWORDS:

• Biomanufacturing
• biodegradation
• constrained Gaussian process
• censoring
• monotonicity
• predictive modeling

Additional information

Funding

Li Zeng gratefully acknowledges financial support from the National Science Foundation under grant CMMI-1649009.

Notes on contributors

Li Zeng

Li Zeng is an assistant professor in the Department of Industrial and Systems Engineering at Texas A&M University. She received her B.S. degree in precision instruments; M.S. degree in optical engineering from Tsinghua University, China; and Ph.D. in industrial engineering and M.S. degree in statistics from the University of Wisconsin–Madison. Her research interests are systems informatics and process control in complex manufacturing and healthcare delivery systems. She is a member of INFORMS and IISE.

Xinwei Deng

Xinwei Deng is an associate professor in the Department of Statistics at Virginia Tech. He received his Ph.D. degree in industrial engineering from Georgia Tech and his bachelor's degree in mathematics from Nanjing University, China. His research interests are in statistical modeling and analysis of massive data, including high-dimensional classification, graphical model estimation, interface between experimental design and machine learning, and statistical approaches to nanotechnology. He is a member of INFORMS and ASA.

Jian Yang

Jian Yang is a professor of biomedical engineering at the Pennsylvania State University. He is known as the inventor for citrate-based biomaterials for tissue engineering and medical devices. He has published 71 journal articles with many shown in prestigious journals such as PNAS, Advanced Materials, and ACS Nano. He has also received eight issued patents for his inventions in citrate polymers and their applications. He was a recipient of an NSF CAREER Award (2010) and Outstanding Young Faculty Award of College of Engineering at University of Texas Arlington (2011). He serves as an associate editor for Frontiers in Biomaterials and sits on the editorial board for a number of journals in his field.