Machine-Learning-Based Prediction of Chinese Hamster Ovary Cell Stability Due to Epigenetic Changes
Seber, P.; Braatz, R. D.
Show abstract
Chinese hamster ovary (CHO) cells are the main system for producing biopharmaceuticals, but they suffer from instability, affecting their long-term productivity. This instability prevents their use in perfusion bioreactors, which are more productive, and increases the costs of biopharmaceuticals. In this work, we create the first models for predicting long-term CHO cell stability due to changes in chromatin modification levels and methylation. Multilayer perceptrons are the best-performing models, reaching an F1 score of 59.1% and a Matthews correlation co-efficient of 19.4% on this task. Furthermore, Shapley values and inter-pretable models are used to investigate model coefficients, contributing biological insight to this problem and helping focus future data collection efforts. We freely provide the models trained in this work.
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