• Mitchell T.
• et al.

Machine learning.

Annu. Rev. Comput. Sci. 1990; 4: 417-433

• Ender T.R.
• Balestrini-Robinson S.

Surrogate modeling.

in: Loper M.L. Modeling and Simulation in the Systems Engineering Life Cycle. Springer, 2015: 201-216

• Miller T.H.
• et al.

Machine learning for environmental toxicology: a call for integration and innovation.

Environ. Sci. Technol. 2018; 52: 12953-12955

• Bonetta R.
• Valentino G.

Machine learning techniques for protein function prediction.

Proteins. 2020; 88: 397-413

• Reel P.S.
• et al.

Using machine learning approaches for multi-omics data analysis: a review.

Biotechnol. Adv. 2021; 49107739

• Villoutreix P.

What machine learning can do for developmental biology.

Development. 2021; 148: dev188474

• Muzio G.
• et al.

Biological network analysis with deep learning.

Brief. Bioinform. 2021; 22: 1515-1530

• Volk M.J.
• et al.

Biosystems design by machine learning.

ACS Synth. Biol. 2020; 9: 1514-1533

• Mowbray M.
• et al.

Machine learning for biochemical engineering: a review.

Biochem. Eng. J. 2021; 172108054

• Angermueller C.
• et al.

Deep learning for computational biology.

Mol. Syst. Biol. 2016; 12: 878

• Jumper J.
• et al.

Highly accurate protein structure prediction with AlphaFold.

Nature. 2021; 596: 583-589

• Walters W.P.
• Barzilay R.

Applications of deep learning in molecule generation and molecular property prediction.

Acc. Chem. Res. 2021; 54: 263-270

• Butler K.T.
• et al.

Machine learning for molecular and materials science.

Nature. 2018; 559: 547-555

• Ding Y.
• et al.

Machine learning approaches for predicting biomolecule-disease associations.

Brief. Funct. Genomics. 2021; 20: 273-287

• Graves J.
• et al.

A review of deep learning methods for antibodies.

Antibodies (Basel). 2020; 9: 12

• Leavell M.D.
• et al.

High-throughput screening for improved microbial cell factories, perspective and promise.

Curr. Opin. Biotechnol. 2020; 62: 22-28

• Silva T.C.
• et al.

Automation and miniaturization: enabling tools for fast, high-throughput process development in integrated continuous biomanufacturing.

J. Chem. Technol. Biotechnol. 2021; 97: 2365-2375

• Wasalathanthri D.P.
• et al.

Process analytics 4.0: a paradigm shift in rapid analytics for biologics development.

Biotechnol. Prog. 2021; 37e3177

• Carbonell P.
• et al.

An automated design-build-test-learn pipeline for enhanced microbial production of fine chemicals.

Commun. Biol. 2018; 1: 66-69

• Opgenorth P.
• et al.

Lessons from two design-build-test-learn cycles of dodecanol production in Escherichia coli aided by machine learning.

ACS Synth. Biol. 2019; 8: 1337-1351

• Liao X.
• et al.

Artificial intelligence: a solution to involution of design-build-test-learn cycle.

Curr. Opin. Biotechnol. 2022; 75102712

• Dickens J.
• et al.

Biopharmaceutical raw material variation and control.

Curr. Opin. Chem. Eng. 2018; 22: 236-243

• Jordan M.
• et al.

Intensification of large-scale cell culture processes.

Curr. Opin. Chem. Eng. 2018; 22: 253-257

• von Stosch M.
• et al.

A roadmap to AI-driven in silico process development: bioprocessing 4.0 in practice.

Curr. Opin. Chem. Eng. 2021; 33100692

• Artico F.
• et al.

The future of artificial intelligence for the BioTech big data landscape.

Curr. Opin. Biotechnol. 2022; 76102714

• Joshi V.S.
• et al.

Optimization of ion exchange sigmoidal gradients using hybrid models: implementation of quality by design in analytical method development.

J. Chromatogr. A. 2017; 1491: 145-152

• Wang G.
• et al.

Root cause investigation of deviations in protein chromatography based on mechanistic models and artificial neural networks.

J. Chromatogr. A. 2017; 1515: 146-153

• Brestrich N.
• et al.

Selective protein quantification for preparative chromatography using variable pathlength UV/Vis spectroscopy and partial least squares regression.

Chem. Eng. Sci. 2018; 176: 157-164

• Risum A.B.
• Bro R.

Using deep learning to evaluate peaks in chromatographic data.

Talanta. 2019; 204: 255-260

• Kensert A.
• et al.

Deep Q-learning for the selection of optimal isocratic scouting runs in liquid chromatography.

J. Chromatogr. A. 2021; 1638461900

• Vaskevicius M.
• et al.

Prediction of chromatography conditions for purification in organic synthesis using deep learning.

Molecules. 2021; 26: 2474

• Liu S.
• Papageorgiou L.G.

Optimal antibody purification strategies using data-driven models.

Engineering. 2019; 5: 1077-1092

• Walther C.
• et al.

Smart process development: application of machine-learning and integrated process modeling for inclusion body purification processes.

Biotechnol. Prog. 2022; 38e3249

• Wehrs M.
• et al.

You get what you screen for: on the value of fermentation characterization in high-throughput strain improvements in industrial settings.

J. Ind. Microbiol. Biotechnol. 2020; 47: 913-927

• Hemmerich J.
• et al.

Microbioreactor systems for accelerated bioprocess development.

Biotechnol. J. 2018; 13e1700141

• Grav L.M.
• et al.

Minimizing clonal variation during mammalian cell line engineering for improved systems biology data generation.

ACS Synth. Biol. 2018; 7: 2148-2159

• McKinley K.L.
• Cheeseman I.M.

Large-scale analysis of CRISPR/Cas9 cell-cycle knockouts reveals the diversity of p53-dependent responses to cell-cycle defects.

Dev. Cell. 2017; 40: 405-420

• Mazurenko S.
• et al.

Machine learning in enzyme engineering.

ACS Catal. 2019; 10: 1210-1223

• Siedhoff N.E.
• et al.

Machine learning-assisted enzyme engineering.

Methods Enzymol. 2020; 643: 281-315

• Gu C.
• et al.

Current status and applications of genome-scale metabolic models.

Genome Biol. 2019; 20: 121

• Srinivasan S.
• et al.

Constructing kinetic models of metabolism at genome-scales: a review.

Biotechnol. J. 2015; 10: 1345-1359

• Almquist J.
• et al.

Kinetic models in industrial biotechnology – improving cell factory performance.

Metab. Eng. 2014; 24: 38-60

• Stalidzans E.
• et al.

Model-based metabolism design: constraints for kinetic and stoichiometric models.

Biochem. Soc. Trans. 2018; 46: 261-267

• Heirendt L.
• et al.

Creation and analysis of biochemical constraint-based models using the COBRA Toolbox v.3.0.

Nat. Protoc. 2019; 14: 639-702

• Oyetunde T.
• et al.

Leveraging knowledge engineering and machine learning for microbial bio-manufacturing.

Biotechnol. Adv. 2018; 36: 1308-1315

• Orth J.D.
• et al.

What is flux balance analysis?.

Nat. Biotechnol. 2010; 28: 245-248

• Segre D.
• et al.

Analysis of optimality in natural and perturbed metabolic networks.

Proc. Natl. Acad. Sci. U. S. A. 2002; 99: 15112-15117

• Schneider P.
• et al.

An extended and generalized framework for the calculation of metabolic intervention strategies based on minimal cut sets.

PLoS Comput. Biol. 2020; 16e1008110

• Mishra B.
• et al.

Systems biology and machine learning in plant-pathogen interactions.

Mol. Plant-Microbe Interact. 2019; 32: 45-55

• Rana P.
• et al.

Recent advances on constraint-based models by integrating machine learning.

Curr. Opin. Biotechnol. 2020; 64: 85-91

• King Z.A.
• et al.

Literature mining supports a next-generation modeling approach to predict cellular byproduct secretion.

Metab. Eng. 2017; 39: 220-227

• Oyetunde T.
• et al.

Machine learning framework for assessment of microbial factory performance.

PLoS One. 2019; 14e0210558

• Zhang J.
• et al.

Combining mechanistic and machine learning models for predictive engineering and optimization of tryptophan metabolism.

Nat. Commun. 2020; 11: 4880

• Radivojevic T.
• et al.

A machine learning automated recommendation tool for synthetic biology.

Nat. Commun. 2020; 11: 4879

• Pedregosa F.
• et al.

Scikit-learn: machine learning in python.

J. Mach. Learn. Res. 2011; 12: 2825-2830

• Carbonell P.
• et al.

Opportunities at the intersection of synthetic biology, machine learning, and automation.

ACS Synth. Biol. 2019; 8: 1474-1477

• Faure L.
• et al.

Artificial metabolic networks: enabling neural computation with metabolic networks.

bioRxiv. 2022; ()

• Zampieri G.
• et al.

Machine and deep learning meet genome-scale metabolic modeling.

PLoS Comput. Biol. 2019; 15e1007084

• Antonakoudis A.
• et al.

The era of big data: genome-scale modelling meets machine learning.

Comput. Struct. Biotechnol. J. 2020; 18: 3287-3300

• van Rosmalen R.P.
• et al.

Model reduction of genome-scale metabolic models as a basis for targeted kinetic models.

Metab. Eng. 2021; 64: 74-84

• Choudhury S.
• et al.

Reconstructing kinetic models for dynamical studies of metabolism using generative adversarial networks.

Nat. Mach. Intell. 2022; 4: 710-719

• Sabzevari M.
• et al.

Strain design optimization using reinforcement learning.

PLoS Comput. Biol. 2022; 18e1010177

• Wu S.G.
• et al.

Rapid prediction of bacterial heterotrophic fluxomics using machine learning and constraint programming.

PLoS Comput. Biol. 2016; 12e1004838

• Li G.
• et al.

Machine learning applied to predicting microorganism growth temperatures and enzyme catalytic optima.

ACS Synth. Biol. 2019; 8: 1411-1420

• Bradford E.
• et al.

Dynamic modeling and optimization of sustainable algal production with uncertainty using multivariate Gaussian processes.

Comput. Chem. Eng. 2018; 118: 143-158

• Vega-Ramon F.
• et al.

Kinetic and hybrid modeling for yeast astaxanthin production under uncertainty.

Biotechnol. Bioeng. 2021; 118: 4854-4866

• Freier L.
• et al.

Framework for Kriging-based iterative experimental analysis and design: optimization of secretory protein production in Corynebacterium glutamicum.

Eng. Life Sci. 2016; 16: 538-549

• Zheng Z.Y.
• et al.

Artificial neural network – genetic algorithm to optimize wheat germ fermentation condition: application to the production of two anti-tumor benzoquinones.

Food Chem. 2017; 227: 264-270

• del Rio-Chanona E.A.
• et al.

Dynamic modeling and optimization of cyanobacterial C-phycocyanin production process by artificial neural network.

Algal Res. 2016; 13: 7-15

• Pappu S.M.J.
• Gummadi S.N.

Artificial neural network and regression coupled genetic algorithm to optimize parameters for enhanced xylitol production by Debaryomyces nepalensis in bioreactor.

Biochem. Eng. J. 2017; 120: 136-145

• Ebrahimpour A.
• et al.

A modeling study by response surface methodology and artificial neural network on culture parameters optimization for thermostable lipase production from a newly isolated thermophilic Geobacillus sp. strain ARM.

BMC Biotechnol. 2008; 8: 96

• Sebayang A.H.
• et al.

Optimization of bioethanol production from sorghum grains using artificial neural networks integrated with ant colony.

Ind. Crop. Prod. 2017; 97: 146-155

• Rodriguez-Granrose D.
• et al.

Design of experiment (DOE) applied to artificial neural network architecture enables rapid bioprocess improvement.

Bioprocess Biosyst. Eng. 2021; 44: 1301-1308

• Rogers A.W.
• et al.

A transfer learning approach for predictive modeling of bioprocesses using small data.

Biotechnol. Bioeng. 2022; 119: 411-422

• Hutter C.
• et al.

Knowledge transfer across cell lines using hybrid Gaussian process models with entity embedding vectors.

Biotechnol. Bioeng. 2021; 118: 4389-4401

• Wang Y.
• et al.

A comparison of word embeddings for the biomedical natural language processing.

J. Biomed. Inform. 2018; 87: 12-20

• Bluma A.
• et al.

In-situ imaging sensors for bioprocess monitoring: state of the art.

Anal. Bioanal. Chem. 2010; 398: 2429-2438

• Marba-Ardebol A.M.
• et al.

In situ microscopy for real-time determination of single-cell morphology in bioprocesses.

J. Vis. Exp. 2019; ()

• Grunberger A.
• et al.

Single-cell microfluidics: opportunity for bioprocess development.

Curr. Opin. Biotechnol. 2014; 29: 15-23

• Du G.
• et al.

Microfluidics for cell-based high throughput screening platforms – a review.

Anal. Chim. Acta. 2016; 903: 36-50

• Riordon J.
• et al.

Deep learning with microfluidics for biotechnology.

Trends Biotechnol. 2019; 37: 310-324

• Galan E.A.
• et al.

Intelligent microfluidics: the convergence of machine learning and microfluidics in materials science and biomedicine.

Matter. 2020; 3: 1893-1922

• Stallmann D.
• et al.

Towards an automatic analysis of CHO-K1 suspension growth in microfluidic single-cell cultivation.

Bioinformatics. 2021; 37: 3632-3639

• O’Connor O.M.
• et al.

DeLTA 2.0: A deep learning pipeline for quantifying single-cell spatial and temporal dynamics.

PLoS Comput. Biol. 2022; 18e1009797

• Lashkaripour A.
• et al.

Machine learning enables design automation of microfluidic flow-focusing droplet generation.

Nat. Commun. 2021; 12: 25

• Hartmann R.
• et al.

BiofilmQ, a software tool for quantiative image analysis of microbial biofilm communities.

Nat. Microbiol. 2020; 6: 151-156

• Long B.
• et al.

Machine learning-informed and synthetic biology-enabled semi-continuous algal cultivation to unleash renewable fuel productivity.

Nat. Commun. 2022; 13: 541

Calculation of light penetration depth in photobioreactors.

Biotechnol. Bioprocess Eng. 1999; 4: 78-81

• Wang J.
• et al.

The difference in effective light penetration may explain the superiority in photosynthetic efficiency of attached cultivation over the conventional open pond for microalgae.

Biotechnol. Biofuels. 2015; 8: 49

• Göttl Q.
• et al.

Automated flowsheet synthesis using hierarchical reinforcement learning: proof of concept.

Chem. Ing. Tech. 2021; 93: 2010-2018

• Stops L.
• et al.

Flowsheet synthesis through hierarchical reinforcement learning and graph neural networks.

arXiv. 2022; ()

Scale-up of microbial processes: impacts, tools and open questions.

J. Biotechnol. 2012; 160: 3-9

• Neubauer P.
• Junne S.

Scale-up and scale-down methodologies for bioreactors.

in: Mandenius C.-F. Bioreactors. Wiley-VCH Verlag, 2016: 323-354

• Delvigne F.
• et al.

Bioprocess scale-up/down as integrative enabling technology: from fluid mechanics to systems biology and beyond.

Microb. Biotechnol. 2017; 10: 1267-1274

• Wang G.
• et al.

Comparative performance of different scale-down simulators of substrate gradients in Penicillium chrysogenum cultures: the need of a biological systems response analysis.

Microb. Biotechnol. 2018; 11: 486-497

• Karimi Alavijeh M.
• et al.

Digitally enabled approaches for the scale up of mammalian cell bioreactors.

Chem. Eng. Technol. 2022; 4100040

• Le H.
• et al.

Multivariate analysis of cell culture bioprocess data–lactate consumption as process indicator.

J. Biotechnol. 2012; 162: 210-223

• Facco P.
• et al.

Using data analytics to accelerate biopharmaceutical process scale-up.

Biochem. Eng. J. 2020; 164107791

• Smiatek J.
• et al.

Generic and specific recurrent neural network models: applications for large and small scale biopharmaceutical upstream processes.

Biotechnol. Rep. (Amst.). 2021; 31e00640

• Sokolov M.
• et al.

Sequential multivariate cell culture modeling at multiple scales supports systematic shaping of a monoclonal antibody toward a quality target.

Biotechnol. J. 2018; 13e1700461

• Bayer B.
• et al.

Model transferability and reduced experimental burden in cell culture process development facilitated by hybrid modeling and intensified design of experiments.

Front. Bioeng. Biotechnol. 2021; 9740215

• Cai S.
• et al.

Physics-informed neural networks (PINNs) for fluid mechanics: a review.

Acta Mech. Sinica. 2022; 37: 1727-1738

• Mowbray M.
• et al.

Industrial data science – a review of machine learning applications for chemical and process industries.

React. Chem. Eng. 2022; 7: 1471-1509

• Luttmann R.
• et al.

Soft sensors in bioprocessing: a status report and recommendations.

Biotechnol. J. 2012; 7: 1040-1048

• Gerzon G.
• et al.

Process analytical technologies – advances in bioprocess integration and future perspectives.

J. Pharm. Biomed. Anal. 2022; 207114379

• Narayanan H.
• et al.

Bioprocessing in the digital age: the role of process models.

Biotechnol. J. 2020; 15e1900172

• Kadlec P.
• et al.

Data-driven soft sensors in the process industry.

Comput. Chem. Eng. 2009; 33: 795-814

• Desai K.
• et al.

Soft-sensor development for fed-batch bioreactors using support vector regression.

Biochem. Eng. J. 2006; 27: 225-239

• Randek J.
• Mandenius C.F.

On-line soft sensing in upstream bioprocessing.

Crit. Rev. Biotechnol. 2018; 38: 106-121

• Zhu X.
• et al.

Modern soft-sensing modeling methods for fermentation processes.

Sensors (Basel). 2020; 20: 1771

• Schmidt A.
• et al.

Process analytical technology as key-enabler for digital twins in continuous biomanufacturing.

J. Chem. Technol. Biotechnol. 2022; 97: 2336-2346

• Chen Y.
• et al.

Digital twins in pharmaceutical and biopharmaceutical manufacturing: a literature review.

Processes. 2020; 8: 1088

• Hartmann F.S.F.
• et al.

Digital models in biotechnology: towards multi-scale integration and implementation.

Biotechnol. Adv. 2022; 60108015

• Portela R.M.C.
• et al.

When is an in silico representation a digital twin? A biopharmaceutical industry approach to the digital twin concept.

Adv. Biochem. Eng. Biotechnol. 2021; 176: 35-55

• Zobel-Roos S.
• et al.

Digital Twins in Biomanufacturing.

Adv. Biochem. Eng. Biotechnol. 2021; 176: 181-262

• Sun Q.
• Ge Z.

A survey on deep learning for data-driven soft sensors.

IEEE Trans. Industr. Inform. 2021; 17: 5853-5866

• Dai X.
• et al.

“Assumed inherent sensor” inversion based ANN dynamic soft-sensing method and its application in erythromycin fermentation process.

Comput. Chem. Eng. 2006; 30: 1203-1225

• Albiol J.
• et al.

Biomass estimation in plant cell cultures: a neural network approach.

Biotechnol. Prog. 1995; 11: 88-92

• Wang B.
• et al.

Soft-sensor modeling for L-lysine fermentation process based on hybrid ICS-MLSSVM.

Sci. Rep. 2020; 10: 11630

• Graziani S.
• Xibilia M.G.

Deep learning for soft sensor design.

in: Pedrycz W. Chen S.-M. Development and Analysis of Deep Learning Architectures. Springer, 2020: 31-59

• Gopakumar V.
• et al.

A deep learning based data driven soft sensor for bioprocesses.

Biochem. Eng. J. 2018; 136: 28-39

• Yao L.
• Ge Z.

Deep learning of semisupervised process data with hierarchical extreme learning machine and soft sensor application.

IEEE Trans. Ind. Electron. 2018; 65: 1490-1498

• Mowbray M.
• et al.

Probabilistic machine learning based soft-sensors for product quality prediction in batch processes.

Chemom. Intell. Lab. Syst. 2022; 228104616

• Curreri F.
• et al.

Soft sensor transferability: a survey.

Appl. Sci. 2021; 11: 7710

• Kadlec P.
• et al.

Review of adaptation mechanisms for data-driven soft sensors.

Comput. Chem. Eng. 2011; 35: 1-24

• Li W.
• et al.

Transfer learning for process fault diagnosis: knowledge transfer from simulation to physical processes.

Comput. Chem. Eng. 2020; 139106904

• Camacho E.F.
• Alba C.B.

Model Predictive Control.

Springer, 2013

• Hewing L.
• et al.

Learning-based model predictive control: toward safe learning in control.

Annu. Rev. Control Robot. Auton. Syst. 2020; 3: 269-296

• Chee E.
• et al.

An integrated approach for machine-learning-based system identification of dynamical systems under control: application towards the model predictive control of a highly nonlinear reactor system.

Front. Chem. Sci. Eng. 2021; 16: 237-250

Model based control of a yeast fermentation bioreactor using optimally designed artificial neural networks.

Chem. Eng. J. 2007; 127: 95-109

• Masampally V.S.
• et al.

Cascade Gaussian Process Regression Framework for Biomass Prediction in a Fed-batch Reactor.

in: 2018 IEEE Symposium Series on Computational Intelligence (SSCI). 2018

• Zan T.
• et al.

Statistical process control with intelligence based on the deep learning model.

Appl. Sci. 2019; 10: 308

• Petsagkourakis P.
• et al.

Reinforcement learning for batch bioprocess optimization.

Comput. Chem. Eng. 2020; 133106649

• Xie H.
• et al.

Model Predictive Control Guided Reinforcement Learning Control Scheme.

in: 2020 International Joint Conference on Neural Networks (IJCNN). 2020

• Hedrick E.
• et al.

Reinforcement learning for online adaptation of model predictive controllers: application to a selective catalytic reduction unit.

Comput. Chem. Eng. 2022; 160107727

• Treloar N.J.
• et al.

Deep reinforcement learning for the control of microbial co-cultures in bioreactors.

PLoS Comput. Biol. 2020; 16e1007783

• Oh T.H.
• et al.

Integration of reinforcement learning and model predictive control to optimize semi-batch bioreactor.

AIChE J. 2022; 68: 6

• Rehnert M.
• Takors R.

FAIR research data management as community approach in bioengineering.

Eng. Life Sci. 2022; ()

• Wilkinson M.D.
• et al.

The FAIR guiding principles for scientific data management and stewardship.

Sci. Data. 2016; 3160018

• Farid S.S.
• et al.

Benchmarking biopharmaceutical process development and manufacturing cost contributions to R&D.

MAbs. 2020; 12: 1754999

• Faulon J.L.
• Faure L.

In silico, in vitro, and in vivo machine learning in synthetic biology and metabolic engineering.

Curr. Opin. Chem. Biol. 2021; 65: 85-92

• O’Brien C.M.
• et al.

A hybrid mechanistic-empirical model for in silico mammalian cell bioprocess simulation.

Metab. Eng. 2021; 66: 31-40

• Udaondo Z.

Big data and computational advancements for next generation of microbial biotechnology.

Microb. Biotechnol. 2022; 15: 107-109

• Giovani B.

Open data for research and strategic monitoring in the pharmaceutical and biotech industry.

Data Sci. J. 2017; 16: 18

• Gitter D.M.

Resolving the open source paradox in biotechnology: a proposal for a revised open source policy for publicly funded genomic databases.

Comput. Law Secur. Rev. 2008; 24: 529-539

• Sayers E.W.
• et al.

Database resources of the national center for biotechnology information.

Nucleic Acids Res. 2022; 50: D20-D26

• Oliveira A.L.

Biotechnology, big data and artificial intelligence.

Biotechnol. J. 2019; 14e1800613

• Harrow J.
• et al.

ELIXIR-EXCELERATE: establishing Europe’s data infrastructure for the life science research of the future.

EMBO J. 2021; 40e107409

• Kok J.N.
• Unesco

Artificial Intelligence.

Eolss Publishers Company, 2009

• Alpaydin E.

Introduction to Machine Learning.

4th edn. MIT Press, 2020

• Buchanan B.G.
• Smith R.G.

Fundamentals of expert systems.

Annu. Rev. Comput. Sci. 1988; 3: 23-58

• Cunningham P.
• et al.

Supervised learning.

in: Cord M. Cunningham P. Machine Learning Techniques for Multimedia: Case Studies on Organization and Retrieval. Springer, 2008: 21-49

• Ghahramani Z.

Unsupervised learning.

in: Bousquet O. Advanced Lectures on Machine Learning: ML Summer Schools 2003, Canberra, Australia, February 2 – 14, 2003, Tübingen, Germany, August 4 – 16, 2003, Revised Lectures. Springer, 2004: 72-112

• Kaelbling L.P.
• et al.

Reinforcement learning: a survey.

J. Artif. Intell. Res. 1996; 4: 237-285

• Sutton R.S.

Introduction: the challenge of reinforcement learning.

in: Sutton R.S. Reinforcement Learning. Springer, 1992: 1-3

• Weiss K.
• et al.

A survey of transfer learning.

J. Big Data. 2016; 3: 9

• Hua J.
• et al.

Learning for a robot: deep reinforcement learning, imitation learning, transfer learning.

Sensors (Basel). 2021; 21: 1278

• Mahmud M.
• et al.

Applications of deep learning and reinforcement learning to biological data.

IEEE Trans. Neural Netw. Learn. Syst. 2018; 29: 2063-2079

• Voulodimos A.
• et al.

Deep learning for computer vision: a brief review.

Comput. Intell. Neurosci. 2018; 2018: 7068349

• Ching T.
• et al.

Opportunities and obstacles for deep learning in biology and medicine.

J. R. Soc. Interface. 2018; 15: 20170387

• Bennett D.
• et al.

Value-free reinforcement learning: policy optimization as a minimal model of operant behavior.

Curr. Opin. Behav. Sci. 2021; 41: 114-121

• Zhou Z.-H.

Ensemble learning.

in: Mach Learn. Springer, 2021: 181-210

• Lawson C.E.
• et al.

Machine learning for metabolic engineering: a review.

Metab. Eng. 2021; 63: 34-60

• Greener J.G.
• et al.

A guide to machine learning for biologists.

Nat. Rev. Mol. Cell Biol. 2022; 23: 40-55

• Wang S.-C.

Artificial neural network.

in: Interdisciplinary Computing in Java Programming. Springer, 2003: 81-100

• Dhruv P.
• Naskar S.

Image Classification Using Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN): A Review.

Springer, 2020

• Gu J.
• et al.

Recent advances in convolutional neural networks.

Pattern Recogn. 2018; 77: 354-377

• Izmailov P.
• et al.

What are Bayesian neural network posteriors really like?.

in: Marina M. Tong Z. Proceedings of the 38th International Conference on Machine Learning. ICML, 2021

• Goodfellow I.
• et al.

Deep Learning.

MIT Press, 2016

• Connor M.
• et al.

Variational autoencoder with learned latent structure.

in: Arindam B. Kenji F. Proceedings of The 24th International Conference on Artificial Intelligence and Statistics. AAAI Press, 2021

• Basu S.
• et al.

Iterative random forests to discover predictive and stable high-order interactions.

Proc. Natl. Acad. Sci. U. S. A. 2018; 115: 1943-1948

• Noble W.S.

What is a support vector machine?.

Nat. Biotechnol. 2006; 24: 1565-1567

Biological applications of support vector machines.

Brief. Bioinform. 2004; 5: 328-338

• di Sciascio F.
• Amicarelli A.N.

Biomass estimation in batch biotechnological processes by Bayesian Gaussian process regression.

Comput. Chem. Eng. 2008; 32: 3264-3273

• Lan L.
• et al.

Generative adversarial networks and its applications in biomedical informatics.

Front. Public Health. 2020; 8: 164

• Jiao Q.
• Zhang S.

A brief survey of word embedding and its recent development.

in: 2021 IEEE 5th Advanced Information Technology, Electronic and Automation Control Conference. IAEAC, 2021

• Bengio S.
• et al.

Group Sparse Coding.

in: Bengio Y. Advances in Neural Information Processing Systems. 22. Curran Associates, Inc., 2009: 82-89

• Watkins C.J.C.H.
• Dayan P.

Q-learning.

Mach. Learn. 1992; 8: 279-292

• Schwenzer M.
• et al.

Review on model predictive control: an engineering perspective.

Int. J. Adv. Manuf. Technol. 2021; 117: 1327-1349

• Altman R.B.
• et al.

Text mining for biology–the way forward: opinions from leading scientists.

Genome Biol. 2008; 9: S7

• Jensen L.J.
• et al.

Literature mining for the biologist: from information retrieval to biological discovery.

Nat. Rev. Genet. 2006; 7: 119-129

• Pinto J.
• et al.

A general deep hybrid model for bioreactor systems: combining first principles with deep neural networks.

Comput. Chem. Eng. 2022; 165107952

• Nelofer R.
• et al.

Comparison of the estimation capabilities of response surface methodology and artificial neural network for the optimization of recombinant lipase production by E. coli BL21.

J. Ind. Microbiol. Biotechnol. 2012; 39: 243-254

• Wang Y.
• et al.

Optimization of dark fermentation for biohydrogen production using a hybrid artificial neural network (ANN) and response surface methodology (RSM) approach.

Environ. Prog. Sustain. Energy. 2020; 40: 2

• Unni S.
• et al.

Artificial neural network-genetic algorithm (ANN-GA) based medium optimization for the production of human interferon gamma (hIFN-γ) in Kluyveromyces lactis cell factory.

Can. J. Chem. Eng. 2019; 97: 843-858

• Tavasoli T.
• et al.

A robust feeding control strategy adjusted and optimized by a neural network for enhancing of alpha 1-antitrypsin production in Pichia pastoris.

Biochem. Eng. J. 2019; 144: 18-27

• Zhang L.
• et al.

Modeling and optimization of microbial lipid fermentation from cellulosic ethanol wastewater by Rhodotorula glutinis based on the support vector machine.

Bioresour. Technol. 2020; 301122781

• Dong C.
• Chen J.

Optimization of process parameters for anaerobic fermentation of corn stalk based on least squares support vector machine.

Bioresour. Technol. 2019; 271: 174-181

• Kennedy M.J.
• Spooner N.R.

Using fuzzy logic to design fermentation media: a comparison to neural networks and factorial design.

Biotechnol. Tech. 1996; 10: 47-52

• Brunner M.
• et al.

Investigation of the interactions of critical scale-up parameters (pH, pO2 and pCO2) on CHO batch performance and critical quality attributes.

Bioprocess Biosyst. Eng. 2017; 40: 251-263

• Holubar P.

Advanced controlling of anaerobic digestion by means of hierarchical neural networks.

Water Res. 2002; 36: 2582-2588

• Glassey J.
• et al.

Enhanced supervision of recombinant E. coli fermentation via artificial neural networks.

Process Biochem. 1994; 29: 387-398

• Shokry A.
• et al.

Data-driven soft-sensors for online monitoring of batch processes with different initial conditions.

Comput. Chem. Eng. 2018; 118: 159-179

• Wong W.
• et al.

Recurrent neural network-based model predictive control for continuous pharmaceutical manufacturing.

Math. 2018; 6: 6110242

• Barberi G.
• et al.

Anticipated cell lines selection in bioprocess scale-up through machine learning on metabolomics dynamics.

IFAC-PapersOnLine. 2021; 54: 85-90

• Poth M.
• et al.

Extensive evaluation of machine learning models and data preprocessings for Raman modeling in bioprocessing.

J. Raman Spectrosc. 2022; 53: 1580-1591

• Hassan S.
• et al.

Bioprocess data mining using regularized regression and random forests.

BMC Syst. Biol. 2013; 7: S5

• Shrivastava R.
• et al.

Application and evaluation of random forest classifier technique for fault detection in bioreactor operation.

Chem. Eng. Commun. 2017; 204: 591-598

• Probst D.
• et al.

Biocatalysed synthesis planning using data-driven learning.

Nat. Commun. 2022; 13: 964

• Kotidis P.
• Kontoravdi C.

Harnessing the potential of artificial neural networks for predicting protein glycosylation.

Metab. Eng. Commun. 2020; 10e00131

• Nikita S.
• et al.

Reinforcement learning based optimization of process chromatography for continuous processing of biopharmaceuticals.

Chem. Eng. Sci. 2021; 230116171

• Pan E.
• et al.

Constrained Q-learning for batch process optimization.

IFAC-PapersOnLine. 2021; 54: 492-497

• Heidari Baladehi M.
• et al.

Culture-free identification and metabolic profiling of microalgal single cells via ensemble learning of ramanomes.

Anal. Chem. 2021; 93: 8872-8880

• Czajka J.J.
• et al.

Integrated knowledge mining, genome-scale modeling, and machine learning for predicting Yarrowia lipolytica bioproduction.

Metab. Eng. 2021; 67: 227-236

• Mowbray M.
• et al.

Ensemble learning for bioprocess dynamic modelling and prediction.

Biotech. Bioeng. 2022; ()

• Liu Y.
• Gunawan R.

Bioprocess optimization under uncertainty using ensemble modeling.

J. Biotechnol. 2017; 244: 34-44

• Pinto J.
• et al.

A bootstrap-aggregated hybrid semi-parametric modeling framework for bioprocess development.

Bioprocess Biosyst. Eng. 2019; 42: 1853-1865

• Tokuyama K.
• et al.

Data science-based modeling of the lysine fermentation process.

J. Biosci. Bioeng. 2020; 130: 409-415

• Agarwal A.
• et al.

110th Anniversary: ensemble-based machine learning for industrial fermenter classification and foaming control.

Ind. Eng. Chem. Res. 2019; 58: 16719-16729

• Mante J.
• et al.

A heuristic approach to handling missing data in biologics manufacturing databases.

Bioprocess Biosyst. Eng. 2019; 42: 657-663

• Zhang T.
• et al.

Pattern recognition in chemical process flowsheets.

AICHE J. 2019; 65: 592-603

• Coşgun A.
• et al.

Analysis of lipid production from Yarrowia lipolytica for renewable fuel production by machine learning.

Fuel. 2022; 315122817

• Resendis-Antonio O.

Constraint-based modeling.

in: Dubitzky W. Encyclopedia of Systems Biology. Springer, 2013: 494-498

• Kumar V.
• et al.

Design of experiments applications in bioprocessing: concepts and approach.

Biotechnol. Prog. 2014; 30: 86-99

• von Stosch M.
• Willis M.J.

Intensified design of experiments for upstream bioreactors.

Eng. Life Sci. 2017; 17: 1173-1184

• Garetti M.
• et al.

Life cycle simulation for the design of product–service systems.

Comput. Ind. 2012; 63: 361-369

• Chowdhary K.R.

Natural language processing.

in: Fundamentals of Artificial Intelligence. Springer, 2020: 603-649

• Hirschberg J.
• Manning C.D.

Advances in natural language processing.

Science. 2015; 349: 261-266