Hyperbox Mixture Regression for process performance prediction in antibody production
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) m...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-03-01
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| Series: | Digital Chemical Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772508125000055 |
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| author | Ali Nik-Khorasani Thanh Tung Khuat Bogdan Gabrys |
| author_facet | Ali Nik-Khorasani Thanh Tung Khuat Bogdan Gabrys |
| author_sort | Ali Nik-Khorasani |
| collection | DOAJ |
| description | 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 that 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. |
| format | Article |
| id | doaj-art-dedabece25684501a71520e1f21f42df |
| institution | OA Journals |
| issn | 2772-5081 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Digital Chemical Engineering |
| spelling | doaj-art-dedabece25684501a71520e1f21f42df2025-08-20T02:14:28ZengElsevierDigital Chemical Engineering2772-50812025-03-011410022110.1016/j.dche.2025.100221Hyperbox Mixture Regression for process performance prediction in antibody productionAli Nik-Khorasani0Thanh Tung Khuat1Bogdan Gabrys2Department of Electrical Engineering, Ferdowsi University of Mashhad, Mashhad, IranComplex Adaptive Systems Laboratory, Data Science Institute, University of Technology Sydney, Sydney, Australia; Corresponding author.Complex Adaptive Systems Laboratory, Data Science Institute, University of Technology Sydney, Sydney, AustraliaThis 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 that 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.http://www.sciencedirect.com/science/article/pii/S2772508125000055Bioprocess performance predictionNeuro-Fuzzy systemHyperboxRegressionMonoclonal antibodies |
| spellingShingle | Ali Nik-Khorasani Thanh Tung Khuat Bogdan Gabrys Hyperbox Mixture Regression for process performance prediction in antibody production Digital Chemical Engineering Bioprocess performance prediction Neuro-Fuzzy system Hyperbox Regression Monoclonal antibodies |
| title | Hyperbox Mixture Regression for process performance prediction in antibody production |
| title_full | Hyperbox Mixture Regression for process performance prediction in antibody production |
| title_fullStr | Hyperbox Mixture Regression for process performance prediction in antibody production |
| title_full_unstemmed | Hyperbox Mixture Regression for process performance prediction in antibody production |
| title_short | Hyperbox Mixture Regression for process performance prediction in antibody production |
| title_sort | hyperbox mixture regression for process performance prediction in antibody production |
| topic | Bioprocess performance prediction Neuro-Fuzzy system Hyperbox Regression Monoclonal antibodies |
| url | http://www.sciencedirect.com/science/article/pii/S2772508125000055 |
| work_keys_str_mv | AT alinikkhorasani hyperboxmixtureregressionforprocessperformancepredictioninantibodyproduction AT thanhtungkhuat hyperboxmixtureregressionforprocessperformancepredictioninantibodyproduction AT bogdangabrys hyperboxmixtureregressionforprocessperformancepredictioninantibodyproduction |