Constrained Gaussian process with application in tissue-engineering scaffold biodegradation
提出约束高斯过程方法，通过整合专家知识（如删失、单调性、边界约束）来预测组织工程支架的生物降解性能，在案例和模拟研究中比无约束高斯过程和人工神经网络更准确。
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.