# When Bioprocess Engineering Meets Machine Learning: A Survey from the Perspective of Automated Bioprocess Development

arXiv: https://arxiv.org/abs/2209.01083 · HTML: https://mlsanity.com/paper/2209.01083/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 38 of its 238 references that are in the map.
- First posted on arXiv 2 Sep 2022.

## Builds on (most cited first, 30 of 38)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [SQuAD: 100,000+ Questions for Machine Comprehension of Text](https://mlsanity.com/paper/1606.05250/) (2016)
- [Addressing Function Approximation Error in Actor-Critic Methods](https://mlsanity.com/paper/1802.09477/) (2018)
- [Explanation in Artificial Intelligence: Insights from the Social Sciences](https://mlsanity.com/paper/1706.07269/) (2017)
- [End-to-End Training of Deep Visuomotor Policies](https://mlsanity.com/paper/1504.00702/) (2015)
- [Deep Transfer Learning with Joint Adaptation Networks](https://mlsanity.com/paper/1605.06636/) (2016)
- [What is being transferred in transfer learning?](https://mlsanity.com/paper/2008.11687/) (2020)
- [Improving Reproducibility in Machine Learning Research (A Report from the NeurIPS 2019 Reproducibility Program)](https://mlsanity.com/paper/2003.12206/) (2020)
- [What makes ImageNet good for transfer learning?](https://mlsanity.com/paper/1608.08614/) (2016)
- [On Hyperparameter Optimization of Machine Learning Algorithms: Theory and Practice](https://mlsanity.com/paper/2007.15745/) (2020)
- [A Tour of Reinforcement Learning: The View from Continuous Control](https://mlsanity.com/paper/1806.09460/) (2018)
- [PMLB: A Large Benchmark Suite for Machine Learning Evaluation and Comparison](https://mlsanity.com/paper/1703.00512/) (2017)
- [Learning how to Active Learn: A Deep Reinforcement Learning Approach](https://mlsanity.com/paper/1708.02383/) (2017)
- [A Step Toward Quantifying Independently Reproducible Machine Learning Research](https://mlsanity.com/paper/1909.06674/) (2019)
- [Scientific Machine Learning Benchmarks](https://mlsanity.com/paper/2110.12773/) (2021)
- [A Unified Stochastic Gradient Approach to Designing Bayesian-Optimal Experiments](https://mlsanity.com/paper/1911.00294/) (2019)
- [PMLB v1.0: An open source dataset collection for benchmarking machine learning methods](https://mlsanity.com/paper/2012.00058/) (2020)
- [Bayesian Experimental Design for Implicit Models by Mutual Information Neural Estimation](https://mlsanity.com/paper/2002.08129/) (2020)
- [Learning protein sequence embeddings using information from structure](https://mlsanity.com/paper/1902.08661/) (2019)
- [Assuring the Machine Learning Lifecycle: Desiderata, Methods, and Challenges](https://mlsanity.com/paper/1905.04223/) (2019)
- [How to Initialize your Network? Robust Initialization for WeightNorm & ResNets](https://mlsanity.com/paper/1906.02341/) (2019)
- [Molecular Design in Synthetically Accessible Chemical Space via Deep Reinforcement Learning](https://mlsanity.com/paper/2004.14308/) (2020)
- [Beyond variance reduction: Understanding the true impact of baselines on policy optimization](https://mlsanity.com/paper/2008.13773/) (2020)
- [Training Stronger Baselines for Learning to Optimize](https://mlsanity.com/paper/2010.09089/) (2020)
- [Reinforcement Learning for Batch Bioprocess Optimization](https://mlsanity.com/paper/1904.07292/) (2019)
- [A Benchmark Study on Time Series Clustering](https://mlsanity.com/paper/2004.09546/) (2020)
- [Dropout-based Active Learning for Regression](https://mlsanity.com/paper/1806.09856/) (2018)
- [Stochastic data-driven model predictive control using Gaussian processes](https://mlsanity.com/paper/1908.01786/) (2019)

## Built on by (most cited first, 4 of 4)

- [Machine Learning Methods for Small Data and Upstream Bioprocessing Applications: A Comprehensive Review](https://mlsanity.com/paper/2506.12322/) (2025)
- [Applications of Machine Learning in Biopharmaceutical Process Development and Manufacturing: Current Trends, Challenges, and Opportunities](https://mlsanity.com/paper/2310.09991/) (2023)
- [Deep Set Neural Networks for forecasting asynchronous bioprocess timeseries](https://mlsanity.com/paper/2312.02079/) (2023)
- [Quantum Synthetic Data Generation for Industrial Bioprocess Monitoring](https://mlsanity.com/paper/2510.17688/) (2025)

## Abstract (arXiv)

Machine learning (ML) is becoming increasingly crucial in many fields of engineering but has not yet played out its full potential in bioprocess engineering. While experimentation has been accelerated by increasing levels of lab automation, experimental planning and data modeling are still largerly depend on human intervention. ML can be seen as a set of tools that contribute to the automation of the whole experimental cycle, including model building and practical planning, thus allowing human experts to focus on the more demanding and overarching cognitive tasks. First, probabilistic programming is used for the autonomous building of predictive models. Second, machine learning automatically assesses alternative decisions by planning experiments to test hypotheses and conducting investigations to gather informative data that focus on model selection based on the uncertainty of model predictions. This review provides a comprehensive overview of ML-based automation in bioprocess development. On the one hand, the biotech and bioengineering community should be aware of the potential and, most importantly, the limitation of existing ML solutions for their application in biotechnology and biopharma. On the other hand, it is essential to identify the missing links to enable the easy implementation of ML and Artificial Intelligence (AI) tools in valuable solutions for the bio-community.