mlsanityData to 7 Oct 2026

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

arXiv 2209.01083 ↗First posted 2 Sep 2022cs.LGcited by 4

Not on the map. Why?

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.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 38 of its 238 references that are in the map. First posted on arXiv 2 Sep 2022.

Builds on

38 of 238 references.

References · 38

  1. 2014Microsoft COCO: Common Objects in Contextcited by 17,498
  2. 2014ImageNet Large Scale Visual Recognition Challengecited by 8,441
  3. 2016XGBoost: A Scalable Tree Boosting Systemcited by 5,120
  4. 2016SQuAD: 100,000+ Questions for Machine Comprehension of Textcited by 4,115
  5. 2018Addressing Function Approximation Error in Actor-Critic Methodscited by 2,302
  6. 2017Explanation in Artificial Intelligence: Insights from the Social Sciencescited by 1,201
  7. 2015End-to-End Training of Deep Visuomotor Policiescited by 847
  8. 2016Deep Transfer Learning with Joint Adaptation Networkscited by 609
  9. 2020What is being transferred in transfer learning?cited by 365
  10. 2020Improving Reproducibility in Machine Learning Research (A Report from the NeurIPS 2019 Reproducibility Program)cited by 318
  11. 2016What makes ImageNet good for transfer learning?cited by 192
  12. 2020On Hyperparameter Optimization of Machine Learning Algorithms: Theory and Practicecited by 181
26 more
  1. 2017PMLB: A Large Benchmark Suite for Machine Learning Evaluation and Comparisoncited by 139
  2. 2018A Tour of Reinforcement Learning: The View from Continuous Controlcited by 129
  3. 2017Learning how to Active Learn: A Deep Reinforcement Learning Approachcited by 79
  4. 2019A Step Toward Quantifying Independently Reproducible Machine Learning Researchcited by 77
  5. 2021Scientific Machine Learning Benchmarkscited by 60
  6. 2019A Unified Stochastic Gradient Approach to Designing Bayesian-Optimal Experimentscited by 58
  7. 2020PMLB v1.0: An open source dataset collection for benchmarking machine learning methodscited by 58
  8. 2020Bayesian Experimental Design for Implicit Models by Mutual Information Neural Estimationcited by 56
  9. 2019Learning protein sequence embeddings using information from structurecited by 50
  10. 2019Assuring the Machine Learning Lifecycle: Desiderata, Methods, and Challengescited by 39
  11. 2019How to Initialize your Network? Robust Initialization for WeightNorm & ResNetscited by 36
  12. 2020Molecular Design in Synthetically Accessible Chemical Space via Deep Reinforcement Learningcited by 32
  13. 2020Beyond variance reduction: Understanding the true impact of baselines on policy optimizationcited by 32
  14. 2020Training Stronger Baselines for Learning to Optimizecited by 31
  15. 2020A Benchmark Study on Time Series Clusteringcited by 25
  16. 2018Dropout-based Active Learning for Regressioncited by 24
  17. 2019Reinforcement Learning for Batch Bioprocess Optimizationcited by 24
  18. 2019Stochastic data-driven model predictive control using Gaussian processescited by 19
  19. 2020The role of metadata in reproducible computational researchcited by 10
  20. 2020Chance Constrained Policy Optimization for Process Control and Optimizationcited by 10
  21. 2021Safe Chance Constrained Reinforcement Learning for Batch Process Controlcited by 10
  22. 2021NAS-HPO-Bench-II: A Benchmark Dataset on Joint Optimization of Convolutional Neural Network Architecture and Training Hyperparameterscited by 9
  23. 2017DLPaper2Code: Auto-generation of Code from Deep Learning Research Paperscited by 8
  24. 2020High-throughput molecular imaging via deep learning enabled Raman spectroscopycited by 8
  25. 2020Constrained Model-Free Reinforcement Learning for Process Optimizationcited by 8
  26. 2020A Performance-Explainability Framework to Benchmark Machine Learning Methods: Application to Multivariate Time Series Classifierscited by 6

Built on by

Cited by year

Citing papers per year: 2023: 2; 2024: 0; 2025: 2.

4 papers.

  1. 2025Machine Learning Methods for Small Data and Upstream Bioprocessing Applications: A Comprehensive Reviewcited by 4
  2. 2023Applications of Machine Learning in Biopharmaceutical Process Development and Manufacturing: Current Trends, Challenges, and Opportunitiescited by 2
  3. 2023Deep Set Neural Networks for forecasting asynchronous bioprocess timeseriescited by 0
  4. 2025Quantum Synthetic Data Generation for Industrial Bioprocess Monitoringcited by 0