# Knowledge transfer across cell lines using Hybrid Gaussian Process models with entity embedding vectors

arXiv: https://arxiv.org/abs/2011.13863 · HTML: https://mlsanity.com/paper/2011.13863/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Nov 2020.

## Built on by (most cited first, 7 of 7)

- [Machine learning in bioprocess development: From promise to practice](https://mlsanity.com/paper/2210.02200/) (2022)
- [Machine Learning Methods for Small Data and Upstream Bioprocessing Applications: A Comprehensive Review](https://mlsanity.com/paper/2506.12322/) (2025)
- [Uncertainty Quantification Using Ensemble Learning and Monte Carlo Sampling for Performance Prediction and Monitoring in Cell Culture Processes](https://mlsanity.com/paper/2409.02149/) (2024)
- [Multi-fidelity batch Bayesian optimization for bioprocess development across scales](https://mlsanity.com/paper/2508.10970/) (2025)
- [Multi-fidelity Gaussian Process for Biomanufacturing Process Modeling with Small Data](https://mlsanity.com/paper/2211.14493/) (2022)
- [A Guide to Bayesian Optimization in Bioprocess Engineering](https://mlsanity.com/paper/2508.10642/) (2025)
- [Transfer Learning Bayesian Optimization to Design Competitor DNA Molecules for Use in Diagnostic Assays](https://mlsanity.com/paper/2402.17704/) (2024)

## Abstract (arXiv)

To date, a large number of experiments are performed to develop a biochemical process. The generated data is used only once, to take decisions for development. Could we exploit data of already developed processes to make predictions for a novel process, we could significantly reduce the number of experiments needed. Processes for different products exhibit differences in behaviour, typically only a subset behave similar. Therefore, effective learning on multiple product spanning process data requires a sensible representation of the product identity. We propose to represent the product identity (a categorical feature) by embedding vectors that serve as input to a Gaussian Process regression model. We demonstrate how the embedding vectors can be learned from process data and show that they capture an interpretable notion of product similarity. The improvement in performance is compared to traditional one-hot encoding on a simulated cross product learning task. All in all, the proposed method could render possible significant reductions in wet-lab experiments.