Pedro Seber

Machine Learning-based Prediction of the Long-term Stability of Chinese Hamster Ovary Cells due to Epigenetic Changes

This paper develops machine learning models to predict long-term stability of Chinese hamster ovary cells using epigenetic data, achieving moderate performance and providing biological insights.

Linear and Neural Network Models for Predicting N-glycosylation in Chinese Hamster Ovary Cells Based on B4GALT Levels

The paper develops linear and neural network models to predict N-glycosylation site glycan distribution in Chinese Hamster Ovary cells using B4GALT levels, achieving a 1.39% median prediction error.

Recurrent Neural Network-based Prediction of O-GlcNAcylation Sites in Mammalian Proteins

The paper develops RNN and MLP models to predict O-GlcNAcylation sites in mammalian proteins, achieving significantly better performance metrics than previous models.

Improving N-Glycosylation and Biopharmaceutical Production Predictions Using AutoML-Built Residual Hybrid Models

This paper develops residual hybrid models combining mechanistic and machine learning to improve predictions of N-glycosylation and biopharmaceutical production, achieving significantly lower prediction errors.