2024/11/03 by Ali Nik-Khorasani, Nik-Khorasani, Ali, Thanh Tung Khuat +3
Biochemistry, Genetics and Molecular Biology · Chemistry · #68T01 #68T05 #68T30 #68T37 #Analytical Chemistry and Chromatography #Computational Engineering #FOS: Biological sciences #FOS: Computer and information sciences #Finance #I.2.4 #I.2.6 #I.5.1 #I.5.4 #J.3 #Machine Learning (cs.LG) #Protein purification and stability #Quantitative Methods (q-bio.QM) #Viral Infectious Diseases and Gene Expression in Insects #and Science (cs.CE)
paper · pdf · doi:10.48550/arxiv.2411.01404
openalex publication_date 2024/11/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
This paper addresses the challenges of predicting bioprocess performance, particularly in monoclonal antibody (mAb) production, where conventional statistical methods often fall short due to time-series data's complexity and high dimensionality. We propose a novel Hyperbox Mixture Regression (HMR) model which employs hyperbox-based input space partitioning to enhance predictive accuracy while managing uncertainty inherent in bioprocess data. The HMR model is designed to dynamically generate hyperboxes for input samples in a single-pass process, thereby improving learning speed and reducing computational complexity. Our experimental study utilizes a dataset that contains 106 bioreactors. This study evaluates the model's performance in predicting critical quality attributes in monoclonal antibody manufacturing over a 15-day cultivation period. The results demonstrate that the HMR model outperforms comparable approximators in accuracy and learning speed and maintains interpretability and robustness under uncertain conditions. These findings underscore the potential of HMR as a powerful tool for enhancing predictive analytics in bioprocessing applications.