While biomanufacturing plays a significant role in supporting the economy and ensuring public health, it faces critical challenges, including complexity, high variability, lengthy lead time, and very limited process data, especially for personalized new cell and gene biotherapeutics. Driven by these challenges, we propose an interpretable semantic bioprocess probabilistic knowledge graph and develop a game theory based risk and sensitivity analyses for production process to facilitate quality-by-design and stability control. Specifically, by exploring the causal relationships and interactions of critical process parameters and quality attributes (CPPs/CQAs), we create a Bayesian network based probabilistic knowledge graph characterizing the complex causal interdependencies of all factors. Then, we introduce a Shapley value based sensitivity analysis, which can correctly quantify the variation contribution from each input factor on the outputs (i.e., productivity, product quality). Since the bioprocess model coefficients are learned from limited process observations, we derive the Bayesian posterior distribution to quantify model uncertainty and further develop the Shapley value based sensitivity analysis to evaluate the impact of estimation uncertainty from each set of model coefficients. Therefore, the proposed bioprocess risk and sensitivity analyses can identify the bottlenecks, guide the reliable process specifications and the most "informative" data collection, and improve production stability.
Uncertainty quantification in model discovery by distilling interpretable material constitutive models from Gaussian process posteriors
Technische Universität Braunschweig, Institute of Applied Mechanics（Braunschweig 技术大学应用力学研究所）；Technische Universität Braunschweig, Institute for Acoustics（Braunschweig 技术大学声学研究所）
A Bayesian Reinterpretation of Cornfield-Type Sensitivity Analysis: From Thresholds to Probabilities
GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin（都灵大学心理系 GCS-fMRI 科埃利凯尔医院）；FOCUS Laboratory, Department of Psychology, University of Turin（都灵大学心理系 FOCUS 实验室）；Neuroscience Institute of Turin (NIT)（都灵神经科学研究所）
Which Hyperparameters Matter? A Game-Theoretic Framework for Interpretable Hyperparameter Sensitivity Analysis
Department of Mechanical and Industrial Engineering（机械与工业工程系）；Louisiana State University（路易斯安那州立大学）；Department of Electrical and Computer Engineering（电气与计算机工程系）
