* Dudek et al. [2016] Dudek, N. L., Croft, N. P., Schittenhelm, R. B., Ramarathinam, S. H., & Purcell, A. W. (2016).  A systems approach to understand antigen presentation and the immune response.  In Proteomics in Systems Biology (pp. 189–209).  Springer.
  * Dumarey et al. [2018] Dumarey, M., Hermanto, M., Airiau, C., Shapland, P., Robinson, H., Hamilton, P., & Berry, M. (2018).  Advances in continuous active pharmaceutical ingredient (API) manufacturing: Real-time monitoring using multivariate tools.  Journal of Pharmaceutical Innovation, 14, 359–372. doi:10.1007/s12247-018-9348-7.
  * Dunbar et al. [2014] Dunbar, J., Krawczyk, K., Leem, J., Baker, T., Fuchs, A., Georges, G., Shi, J., & Deane, C. M. (2014).  Sabdab: the structural antibody database.  Nucleic acids research, 42, D1140–D1146.
  * Duong-Trung et al. [2022] Duong-Trung, N., Born, S., Kim, J. W., Schermeyer, M.-T., Paulick, K., Borisyak, M., Martinez, E., Cruz-Bournazou, M. N., Werner, T., Scholz, R., Schmidt-Thieme, L., & Neubauer, P. (2022).  When bioprocess engineering meets machine learning: A survey from the perspective of automated bioprocess development.  ArXiv, . doi:10.48550/ARXIV.2209.01083.
  * Dutton [2021] Dutton, G. (2021).  Automation will dominate single-use bioprocessing within the next five years.  URL: https://www.labmanager.com/big-picture/bioprocessing-overview-and-trends/automation-will-dominate-single-use-bioprocessing-within-the-next-five-years-25965.
  * Eguchi et al. [2022] Eguchi, R. R., Choe, C. A., & Huang, P.-S. (2022).  Ig-vae: Generative modeling of protein structure by direct 3d coordinate generation.  PLoS computational biology, 18, e1010271:1–18.
  * EL-Manzalawy et al. [2008] EL-Manzalawy, Y., Dobbs, D., & Honavar, V. (2008).  Predicting linear b-cell epitopes using string kernels.  Journal of Molecular Recognition: An Interdisciplinary Journal, 21, 243–255.
  * El-Manzalawy & Honavar [2010] El-Manzalawy, Y., & Honavar, V. (2010).  Recent advances in b-cell epitope prediction methods.  Immunome research, 6, 1–9.
  * Eon-Duval et al. [2012] Eon-Duval, A., Broly, H., & Gleixner, R. (2012).  Quality attributes of recombinant therapeutic proteins: An assessment of impact on safety and efficacy as part of a quality by design development approach.  Biotechnology Progress, 28, 608–622. doi:10.1002/btpr.1548.
  * Esmonde-White et al. [2021] Esmonde-White, K. A., Cuellar, M., & Lewis, I. R. (2021).  The role of raman spectroscopy in biopharmaceuticals from development to manufacturing.  Analytical and Bioanalytical Chemistry, 414, 969–991. doi:10.1007/s00216-021-03727-4.
  * Fahrner et al. [1999] Fahrner, R., Iyer, H., & Blank, G. (1999).  The optimal flow rate and column length for maximum production rate of protein a affinity chromatography.  Bioprocess Engineering, 21, 287–292.
  * Fan et al. [2014] Fan, Y., Val, I. J. D., Müller, C., Sen, J. W., Rasmussen, S. K., Kontoravdi, C., Weilguny, D., & Andersen, M. R. (2014).  Amino acid and glucose metabolism in fed-batch CHO cell culture affects antibody production and glycosylation.  Biotechnology and Bioengineering, 112, 521–535. doi:10.1002/bit.25450.
  * Fang [2020] Fang, J. (2020).  A critical review of five machine learning-based algorithms for predicting protein stability changes upon mutation.  Briefings in bioinformatics, 21, 1285–1292.
  * Farzan et al. [2017] Farzan, P., Mistry, B., & Ierapetritou, M. G. (2017).  Review of the important challenges and opportunities related to modeling of mammalian cell bioreactors.  AIChE Journal, 63, 398–408.
  * Feidl et al. [2019a] Feidl, Garbellini, Luna, Vogg, Souquet, Broly, Morbidelli, & Butté (2019a).  Combining mechanistic modeling and raman spectroscopy for monitoring antibody chromatographic purification.  Processes, 7, 683\. doi:10.3390/pr7100683.
  * Feidl et al. [2019b] Feidl, F., Garbellini, S., Vogg, S., Sokolov, M., Souquet, J., Broly, H., Butté, A., & Morbidelli, M. (2019b).  A new flow cell and chemometric protocol for implementing in-line raman spectroscopy in chromatography.  Biotechnology Progress, 35. doi:10.1002/btpr.2847.
  * Ferdous & Martin [2018] Ferdous, S., & Martin, A. C. (2018).  Abdb: antibody structure database—a database of pdb-derived antibody structures.  Database, 2018.
  * Fidaner et al. [2016] Fidaner, I. B., Cankorur-Cetinkaya, A., Dikicioglu, D., Kirdar, B., Cemgil, A. T., & Oliver, S. G. (2016).  Clusterngo: a user-defined modelling platform for two-stage clustering of time-series data.  Bioinformatics, 32, 388–397.
  * Finn et al. [2017] Finn, C., Abbeel, P., & Levine, S. (2017).  Model-agnostic meta-learning for fast adaptation of deep networks.  In International conference on machine learning (pp. 1126–1135).  PMLR.
  * Folch-Fortuny et al. [2017] Folch-Fortuny, A., Arteaga, F., & Ferrer, A. (2017).  PLS model building with missing data: New algorithms and a comparative study.  Journal of Chemometrics, 31, e2897. doi:10.1002/cem.2897.
  * Friedensohn et al. [2020] Friedensohn, S., Neumeier, D., Khan, T. A., Csepregi, L., Parola, C., de Vries, A. R. G., Erlach, L., Mason, D. M., & Reddy, S. T. (2020).  Convergent selection in antibody repertoires is revealed by deep learning.  BioRxiv, .
  * Gabrys [2002] Gabrys, B. (2002).  Neuro-fuzzy approach to processing inputs with missing values in pattern recognition problems.  International Journal of Approximate Reasoning, 30, 149–179.
  * Gagnon et al. [2011] Gagnon, M., Hiller, G., Luan, Y.-T., Kittredge, A., DeFelice, J., & Drapeau, D. (2011).  High-end pH-controlled delivery of glucose effectively suppresses lactate accumulation in CHO fed-batch cultures.  Biotechnology and Bioengineering, 108, 1328–1337. doi:10.1002/bit.23072.
  * Gainza et al. [2020] Gainza, P., Sverrisson, F., Monti, F., Rodola, E., Boscaini, D., Bronstein, M., & Correia, B. (2020).  Deciphering interaction fingerprints from protein molecular surfaces using geometric deep learning.  Nature Methods, 17, 184–192.
  * Galleguillos et al. [2017] Galleguillos, S. N., Ruckerbauer, D., Gerstl, M. P., Borth, N., Hanscho, M., & Zanghellini, J. (2017).  What can mathematical modelling say about CHO metabolism and protein glycosylation?  Computational and Structural Biotechnology Journal, 15, 212–221. doi:10.1016/j.csbj.2017.01.005.
  * Gambe-Gilbuena et al. [2020] Gambe-Gilbuena, A., Shibano, Y., Krayukhina, E., Torisu, T., & Uchiyama, S. (2020).  Automatic identification of the stress sources of protein aggregates using flow imaging microscopy images.  Journal of Pharmaceutical Sciences, 109, 614–623. doi:10.1016/j.xphs.2019.10.034.
  * Gangadharan et al. [2021] Gangadharan, N., Sewell, D., Turner, R., Field, R., Cheeks, M., Oliver, S. G., Slater, N. K., & Dikicioglu, D. (2021).  Data intelligence for process performance prediction in biologics manufacturing.  Computers and Chemical Engineering, 146, 107226. doi:10.1016/j.compchemeng.2021.107226.
  * Gao et al. [2012] Gao, J., Faraggi, E., Zhou, Y., Ruan, J., & Kurgan, L. (2012).  Best: improved prediction of b-cell epitopes from antigen sequences.  PloS one, 7, e40104.
  * Gautam et al. [2015] Gautam, R., Vanga, S., Ariese, F., & Umapathy, S. (2015).  Review of multidimensional data processing approaches for raman and infrared spectroscopy.  EPJ Techniques and Instrumentation, 2. doi:10.1140/epjti/s40485-015-0018-6.
  * Gentiluomo et al. [2019] Gentiluomo, L., Roessner, D., Augustijn, D., Svilenov, H., Kulakova, A., Mahapatra, S., Winter, G., Streicher, W., Rinnan, Å., Peters, G. H. et al. (2019).  Application of interpretable artificial neural networks to early monoclonal antibodies development.  European Journal of Pharmaceutics and Biopharmaceutics, 141, 81–89.
  * Gentiluomo et al. [2020] Gentiluomo, L., Roessner, D., & Frieß, W. (2020).  Application of machine learning to predict monomer retention of therapeutic proteins after long term storage.  International Journal of Pharmaceutics, 577, 119039. doi:10.1016/j.ijpharm.2020.119039.
  * Georgakis [2013] Georgakis, C. (2013).  Design of dynamic experiments: A data-driven methodology for the optimization of time-varying processes.  Industrial and Engineering Chemistry Research, 52, 12369–12382.
  * Glanville et al. [2015] Glanville, J., D’Angelo, S., Khan, T. A., Reddy, S. T., Naranjo, L., Ferrara, F., & Bradbury, A. R. (2015).  Deep sequencing in library selection projects: what insight does it bring?  Current opinion in structural biology, 33, 146–160.
  * Godawat et al. [2015] Godawat, R., Konstantinov, K., Rohani, M., & Warikoo, V. (2015).  End-to-end integrated fully continuous production of recombinant monoclonal antibodies.  Journal of Biotechnology, 213, 13–19. doi:10.1016/j.jbiotec.2015.06.393.
  * Golabgir et al. [2016] Golabgir, A., Gutierrez, J. M., Hefzi, H., Li, S., Palsson, B. O., Herwig, C., & Lewis, N. E. (2016).  Quantitative feature extraction from the chinese hamster ovary bioprocess bibliome using a novel meta-analysis workflow.  Biotechnology advances, 34, 621–633.
  * Goldrick et al. [2019] Goldrick, S., Duran-Villalobos, C. A., Jankauskas, K., Lovett, D., Farid, S. S., & Lennox, B. (2019).  Modern day monitoring and control challenges outlined on an industrial-scale benchmark fermentation process.  Computers and Chemical Engineering, 130, 106471. doi:10.1016/j.compchemeng.2019.05.037.
  * Goldrick et al. [2018] Goldrick, S., Lee, K., Spencer, C., Holmes, W., Kuiper, M., Turner, R., & Farid, S. S. (2018).  On-line control of glucose concentration in high-yielding mammalian cell cultures enabled through oxygen transfer rate measurements.  Biotechnology Journal, 13, 1700607. doi:10.1002/biot.201700607.
  * Goldrick et al. [2015] Goldrick, S., Ştefan, A., Lovett, D., Montague, G., & Lennox, B. (2015).  The development of an industrial-scale fed-batch fermentation simulation.  Journal of Biotechnology, 193, 70–82. doi:10.1016/j.jbiotec.2014.10.029.
  * Goldrick et al. [2020] Goldrick, S., Umprecht, A., Tang, A., Zakrzewski, R., Cheeks, M., Turner, R., Charles, A., Les, K., Hulley, M., Spencer, C., & Farid, S. S. (2020).  High-throughput raman spectroscopy combined with innovate data analysis workflow to enhance biopharmaceutical process development.  Processes, 8, 1179\. doi:10.3390/pr8091179.
  * Gopaluni et al. [2020] Gopaluni, R. B., Tulsyan, A., Chachuat, B., Huang, B., Lee, J. M., Amjad, F., Damarla, S. K., Kim, J. W., & Lawrence, N. P. (2020).  Modern machine learning tools for monitoring and control of industrial processes: A survey.  IFAC-PapersOnLine, 53, 218–229. doi:10.1016/j.ifacol.2020.12.126.
  * Goulet et al. [2018] Goulet, D. R., Watson, M. J., Tam, S. H., Zwolak, A., Chiu, M. L., Atkins, W. M., & Nath, A. (2018).  Toward a combinatorial approach for the prediction of igg half-life and clearance.  Drug Metabolism and Disposition, 46, 1900–1907.
  * Grand View Research [2022] Grand View Research (2022).  Monoclonal antibodies market size, share & trends analysis report by source type (chimeric, murine, humanized, human), by production type (in vivo, in vitro), by application, by end-use, by region, and segment forecasts, 2022-2030.  URL: https://www.grandviewresearch.com/industry-analysis/monoclonal-antibodies-market.
  * Greiff et al. [2017] Greiff, V., Menzel, U., Miho, E., Weber, C., Riedel, R., Cook, S., Valai, A., Lopes, T., Radbruch, A., Winkler, T. H. et al. (2017).  Systems analysis reveals high genetic and antigen-driven predetermination of antibody repertoires throughout b cell development.  Cell reports, 19, 1467–1478.
  * Grinshpun et al. [2021] Grinshpun, B., Thorsteinson, N., Pereira, J. N., Rippmann, F., Nannemann, D., Sood, V. D., & Fomekong Nanfack, Y. (2021).  Identifying biophysical assays and in silico properties that enrich for slow clearance in clinical-stage therapeutic antibodies.  MAbs, 13, e1932230:1–12.
  * Gronberg et al. [2011] Gronberg, A., Eriksson, M., Ersoy, M., & Johansson, H. J. (2011).  A tool for increasing the lifetime of chromatography resins.  MAbs, 3, 192–202.
  * Großhans et al. [2018] Großhans, S., Rüdt, M., Sanden, A., Brestrich, N., Morgenstern, J., Heissler, S., & Hubbuch, J. (2018).  In-line fourier-transform infrared spectroscopy as a versatile process analytical technology for preparative protein chromatography.  Journal of Chromatography A, 1547, 37–44. doi:10.1016/j.chroma.2018.03.005.
  * Grzesik & Warth [2021] Grzesik, P., & Warth, S. C. (2021).  One-time optimization of advanced t cell culture media using a machine learning pipeline.  Frontiers in bioengineering and biotechnology, 9, 614324.
  * Gu et al. [2019] Gu, Y., Zhang, A. C., Han, Y., Li, J., Chen, C., & Lo, Y.-H. (2019).  Machine learning based real-time image-guided cell sorting and classification.  Cytometry Part A, 95, 499–509. doi:10.1002/cyto.a.23764.
  * Guerra et al. [2019] Guerra, A., von Stosch, M., & Glassey, J. (2019).  Toward biotherapeutic product real-time quality monitoring.  Critical Reviews in Biotechnology, 39, 289–305. doi:10.1080/07388551.2018.1524362.
  * Guerra & Glassey [2018] Guerra, A. C., & Glassey, J. (2018).  Machine learning in biopharmaceutical manufacturing.  European pharmaceutical review, 23, 62–65. URL: https://www.europeanpharmaceuticalreview.com/article/79130/machine-learning-bioprocessing/.
  * Haigney [2013] Haigney, S. (2013).  Qbd and pat in upstream and downstream processing.  BioPharm International, 26, 28–+. URL: https://www.biopharminternational.com/view/qbd-and-pat-upstream-and-downstream-processing.
  * Hakemeyer et al. [2013] Hakemeyer, C., Strauss, U., Werz, S., Folque, F., & Menezes, J. C. (2013).  Near-infrared and two-dimensional fluorescence spectroscopy monitoring of monoclonal antibody fermentation media quality: Aged media decreases cell growth.  Biotechnology Journal, 8, 835–846. doi:10.1002/biot.201200355.
  * Han et al. [2019] Han, X., Zhang, L., Zhou, K., & Wang, X. (2019).  Progan: Protein solubility generative adversarial nets for data augmentation in dnn framework.  Computers & Chemical Engineering, 131, 106533.
  * Hashizume et al. [2022] Hashizume, T., Ozawa, Y., & Ying, B.-W. (2022).  Employing active learning in the optimization of culture medium for mammalian cells.  bioRxiv, .
  * Hassan [2012] Hassan, B. R. (2012).  Overview on pharmaceutical formulation and drug design.  Pharmaceutica Analytica Acta, 3. URL: https://doi.org/10.4172/2153-2435.1000e140.
  * Hebditch & Warwicker [2019] Hebditch, M., & Warwicker, J. (2019).  Charge and hydrophobicity are key features in sequence-trained machine learning models for predicting the biophysical properties of clinical-stage antibodies.  PeerJ, 7, e8199. doi:10.7717/peerj.8199.
  * Hermeling et al. [2004] Hermeling, S., Crommelin, D. J., Schellekens, H., & Jiskoot, W. (2004).  Structure-immunogenicity relationships of therapeutic proteins.  Pharmaceutical research, 21, 897–903.
  * Hiller et al. [2017] Hiller, G. W., Ovalle, A. M., Gagnon, M. P., Curran, M. L., & Wang, W. (2017).  Cell-controlled hybrid perfusion fed-batch cho cell process provides significant productivity improvement over conventional fed-batch cultures.  Biotechnology and bioengineering, 114, 1438–1447.
  * Holmqvist & Magnusson [2016] Holmqvist, A., & Magnusson, F. (2016).  Open-loop optimal control of batch chromatographic separation processes using direct collocation.  Journal of Process Control, 46, 55–74. doi:10.1016/j.jprocont.2016.08.002.
  * Hong et al. [2018] Hong, M. S., Severson, K. A., Jiang, M., Lu, A. E., Love, J. C., & Braatz, R. D. (2018).  Challenges and opportunities in biopharmaceutical manufacturing control.  Computers and Chemical Engineering, 110, 106–114.
  * Huang et al. [2016] Huang, P.-S., Boyken, S. E., & Baker, D. (2016).  The coming of age of de novo protein design.  Nature, 537, 320–327.
  * Hutter et al. [2021] Hutter, C., Stosch, M., Bournazou, M. N. C., & Butté, A. (2021).  Knowledge transfer across cell lines using hybrid gaussian process models with entity embedding vectors.  Biotechnology and Bioengineering, 118, 4389–4401. doi:10.1002/bit.27907.
  * Hutter et al. [2018] Hutter, S., Wolf, M., Gao, N. P., Lepori, D., Schweigler, T., Morbidelli, M., & Gunawan, R. (2018).  Glycosylation flux analysis of immunoglobulin g in chinese hamster ovary perfusion cell culture.  Processes, 6, 176\. doi:10.3390/pr6100176.
  * Igawa et al. [2010] Igawa, T., Tsunoda, H., Tachibana, T., Maeda, A., Mimoto, F., Moriyama, C., Nanami, M., Sekimori, Y., Nabuchi, Y., Aso, Y. et al. (2010).  Reduced elimination of igg antibodies by engineering the variable region.  Protein engineering, design & selection, 23, 385–392.
  * Ingraham et al. [2019] Ingraham, J., Garg, V., Barzilay, R., & Jaakkola, T. (2019).  Generative models for graph-based protein design.  In Proceedings of Advances in Neural Information Processing Systems (NeurIPS).  volume 32.
  * Isu et al. [2022] Isu, S., Qian, X., Zydney, A. L., & Wickramasinghe, S. R. (2022).  Process-and product-related foulants in virus filtration.  Bioengineering, 9, 155.
  * Ivarsson et al. [2014] Ivarsson, M., Villiger, T. K., Morbidelli, M., & Soos, M. (2014).  Evaluating the impact of cell culture process parameters on monoclonal antibody n-glycosylation.  Journal of biotechnology, 188, 88–96.
  * Jain et al. [2017] Jain, T., Sun, T., Durand, S., Hall, A., Houston, N. R., Nett, J. H., Sharkey, B., Bobrowicz, B., Caffry, I., Yu, Y. et al. (2017).  Biophysical properties of the clinical-stage antibody landscape.  Proceedings of the National Academy of Sciences, 114, 944–949.
  * Jarasch et al. [2015] Jarasch, A., Koll, H., Regula, J. T., Bader, M., Papadimitriou, A., & Kettenberger, H. (2015).  Developability assessment during the selection of novel therapeutic antibodies.  Journal of Pharmaceutical Sciences, 104, 1885–1898. doi:10.1002/jps.24430.
  * Jespersen et al. [2019] Jespersen, M. C., Mahajan, S., Peters, B., Nielsen, M., & Marcatili, P. (2019).  Antibody specific b-cell epitope predictions: leveraging information from antibody-antigen protein complexes.  Frontiers in immunology, 10, 298:1–10.
  * Jia & Sun [2017] Jia, L., & Sun, Y. (2017).  Protein asparagine deamidation prediction based on structures with machine learning methods.  PLoS One, 12, e0181347.
  * Jiang et al. [2009] Jiang, C., Liu, J., Rubacha, M., & Shukla, A. A. (2009).  A mechanistic study of protein a chromatography resin lifetime.  Journal of Chromatography A, 1216, 5849–5855.
  * Jiang & Braatz [2016] Jiang, M., & Braatz, R. (2016).  Integrated control of continuous (bio) pharmaceutical manufacturing.  American Pharmaceutical Review, 19, 110–115.
  * Jiang et al. [2017] Jiang, M., Severson, K. A., Love, J. C., Madden, H., Swann, P., Zang, L., & Braatz, R. D. (2017).  Opportunities and challenges of real-time release testing in biopharmaceutical manufacturing.  Biotechnology and Bioengineering, 114, 2445–2456.
  * Jokinen et al. [2018] Jokinen, E., Heinonen, M., & Lähdesmäki, H. (2018).  mgpfusion: predicting protein stability changes with gaussian process kernel learning and data fusion.  Bioinformatics, 34, i274–i283.
  * Jungbauer [2013] Jungbauer, A. (2013).  Continuous downstream processing of biopharmaceuticals.  Trends in Biotechnology, 31, 479–492. doi:10.1016/j.tibtech.2013.05.011.
  * Kadlec & Gabrys [2008] Kadlec, P., & Gabrys, B. (2008).  Adaptive local learning soft sensor for inferential control support.  In 2008 International Conference on Computational Intelligence for Modelling Control & Automation (pp. 243–248).  IEEE.
  * Kadlec & Gabrys [2009a] Kadlec, P., & Gabrys, B. (2009a).  Architecture for development of adaptive on-line prediction models.  Memetic Computing, 1, 241–269.
  * Kadlec & Gabrys [2009b] Kadlec, P., & Gabrys, B. (2009b).  Soft Sensor Based on Adaptive Local Learning.  In M. Köppen, N. Kasabov, & G. Coghill (Eds.), Advances in Neuro-Information Processing: 15th International Conference, ICONIP 2008, Auckland, New Zealand, November 25-28, 2008, Revised Selected Papers, Part I (pp. 1172–1179).  Berlin, Heidelberg: Springer Berlin Heidelberg volume 5506.
  * Kadlec & Gabrys [2009c] Kadlec, P., & Gabrys, B. (2009c).  Soft sensors: where are we and what are the current and future challenges?  IFAC Proceedings Volumes, 42, 572–577.
  * Kadlec & Gabrys [2010] Kadlec, P., & Gabrys, B. (2010).  Adaptive on-line prediction soft sensing without historical data.  In The 2010 International Joint Conference on Neural Networks (IJCNN) (pp. 1–8).  IEEE.
  * Kadlec & Gabrys [2011] Kadlec, P., & Gabrys, B. (2011).  Local learning-based adaptive soft sensor for catalyst activation prediction.  AIChE Journal, 57, 1288–1301.
  * Kadlec et al. [2009] Kadlec, P., Gabrys, B., & Strandt, S. (2009).  Data-driven soft sensors in the process industry.  Computers & chemical engineering, 33, 795–814.
  * Kadlec et al. [2011] Kadlec, P., Grbić, R., & Gabrys, B. (2011).  Review of adaptation mechanisms for data-driven soft sensors.  Computers & chemical engineering, 35, 1–24.
  * Karimi et al. [2020] Karimi, M., Zhu, S., Cao, Y., & Shen, Y. (2020).  De novo protein design for novel folds using guided conditional wasserstein generative adversarial networks.  Journal of chemical information and modeling, 60, 5667–5681.
  * Karst et al. [2016] Karst, D. J., Steinebach, F., Soos, M., & Morbidelli, M. (2016).  Process performance and product quality in an integrated continuous antibody production process.  Biotechnology and Bioengineering, 114, 298–307. doi:10.1002/bit.26069.
  * Kedziora et al. [2020] Kedziora, D. J., Musial, K., & Gabrys, B. (2020).  Autonoml: Towards an integrated framework for autonomous machine learning.  arXiv preprint arXiv:2012.12600, .
  * Keil [2018] Keil, F. J. (2018).  Process intensification.  Reviews in Chemical Engineering, 34, 135–200.
  * Kelley [2009] Kelley, B. (2009).  Industrialization of mab production technology: the bioprocessing industry at a crossroads.  MAbs, 1, 443–452.
  * Kelly et al. [2015] Kelly, R. L., Sun, T., Jain, T., Caffry, I., Yu, Y., Cao, Y., Lynaugh, H., Brown, M., Vásquez, M., Wittrup, K. D. et al. (2015).  High throughput cross-interaction measures for human igg1 antibodies correlate with clearance rates in mice.  MAbs, 7, 770–777.
  * Khetan et al. [2022] Khetan, R., Curtis, R., Deane, C. M., Hadsund, J. T., Kar, U., Krawczyk, K., Kuroda, D., Robinson, S. A., Sormanni, P., Tsumoto, K., Warwicker, J., & Martin, A. C. (2022).  Current advances in biopharmaceutical informatics: guidelines, impact and challenges in the computational developability assessment of antibody therapeutics.  mAbs, 14. doi:10.1080/19420862.2021.2020082.
  * Khuat et al. [2021a] Khuat, T. T., Chen, F., & Gabrys, B. (2021a).  An effective multiresolution hierarchical granular representation based classifier using general fuzzy min-max neural network.  IEEE Transactions on Fuzzy Systems, 29, 427–441.
  * Khuat & Gabrys [2023] Khuat, T. T., & Gabrys, B. (2023).  An online learning algorithm for a neuro-fuzzy classifier with mixed-attribute data.  Applied Soft Computing, 137, 110152.
  * Khuat et al. [2022] Khuat, T. T., Kedziora, D. J., & Gabrys, B. (2022).  The roles and modes of human interactions with automated machine learning systems.  arXiv preprint arXiv:2205.04139, .
  * Khuat et al. [2021b] Khuat, T. T., Ruta, D., & Gabrys, B. (2021b).  Hyperbox-based machine learning algorithms: a comprehensive survey.  Soft Computing, 25, 1325–1363.
  * Khurana et al. [2018] Khurana, S., Rawi, R., Kunji, K., Chuang, G.-Y., Bensmail, H., & Mall, R. (2018).  Deepsol: a deep learning framework for sequence-based protein solubility prediction.  Bioinformatics, 34, 2605–2613.
  * Kim et al. [2021] Kim, J. W., Park, B. J., Oh, T. H., & Lee, J. M. (2021).  Model-based reinforcement learning and predictive control for two-stage optimal control of fed-batch bioreactor.  Computers and Chemical Engineering, 154, 107465. doi:10.1016/j.compchemeng.2021.107465.
  * King et al. [2011] King, A. C., Woods, M., Liu, W., Lu, Z., Gill, D., & Krebs, M. R. H. (2011).  High-throughput measurement, correlation analysis, and machine-learning predictions for pH and thermal stabilities of pfizer-generated antibodies.  Protein Science, 20, 1546–1557.
  * Kirdar et al. [2008] Kirdar, A., Green, K., & Rathore, A. (2008).  Application of multivariate data analysis for identification and successful resolution of a root cause for a bioprocessing application.  Biotechnology Progress, 24, 720–726. doi:10.1021/bp0704384.
  * Kirdar et al. [2007] Kirdar, A. O., Conner, J. S., Baclaski, J., & Rathore, A. S. (2007).  Application of multivariate analysis toward biotech processes: Case study of a cell-culture unit operation.  Biotechnology Progress, 23, 61–67. URL: https://aiche.onlinelibrary.wiley.com/doi/abs/10.1021/bp060377u. doi:https://doi.org/10.1021/bp060377u. arXiv:https://aiche.onlinelibrary.wiley.com/doi/pdf/10.1021/bp060377u.
  * Konakovsky et al. [2017] Konakovsky, V., Clemens, C., Müller, M. M., Bechmann, J., & Herwig, C. (2017).  A robust feeding strategy to maintain set-point glucose in mammalian fed-batch cultures when input parameters have a large error.  Biotechnology Progress, 33, 317--336. doi:10.1002/btpr.2438.
  * Konakovsky et al. [2016] Konakovsky, V., Clemens, C., Müller, M., Bechmann, J., Berger, M., Schlatter, S., & Herwig, C. (2016).  Metabolic control in mammalian fed-batch cell cultures for reduced lactic acid accumulation and improved process robustness.  Bioengineering, 3, 5. doi:10.3390/bioengineering3010005.
  * Konstantinov & Cooney [2015] Konstantinov, K. B., & Cooney, C. L. (2015).  White paper on continuous bioprocessing may 20--21 2014 continuous manufacturing symposium.  Journal of pharmaceutical sciences, 104, 813--820.
  * Kontoravdi & del Val [2018] Kontoravdi, C., & del Val, I. J. (2018).  Computational tools for predicting and controlling the glycosylation of biopharmaceuticals.  Current Opinion in Chemical Engineering, 22, 89--97. doi:10.1016/j.coche.2018.08.007.
  * Kornecki & Strube [2019] Kornecki, M., & Strube, J. (2019).  Accelerating biologics manufacturing by upstream process modelling.  Processes, 7, 166\. doi:10.3390/pr7030166.
  * Kotidis et al. [2019] Kotidis, P., Jedrzejewski, P., Sou, S. N., Sellick, C., Polizzi, K., del Val, I. J., & Kontoravdi, C. (2019).  Model-based optimization of antibody galactosylation in CHO cell culture.  Biotechnology and Bioengineering, 116, 1612--1626. doi:10.1002/bit.26960.
  * Kotidis & Kontoravdi [2020] Kotidis, P., & Kontoravdi, C. (2020).  Harnessing the potential of artificial neural networks for predicting protein glycosylation.  Metabolic Engineering Communications, 10, e00131. doi:10.1016/j.mec.2020.e00131.
  * Kourti [2004] Kourti, T. (2004).  Process analytical technology and multivariate statistical process control: Wellness index of product and process-part 1.  Process Analytical Technology, 1, 13--19.
  * Kourti [2006] Kourti, T. (2006).  Process analytical technology beyond real-time analyzers: The role of multivariate analysis.  Critical Reviews in Analytical Chemistry, 36, 257--278. URL: https://doi.org/10.1080/10408340600969957. doi:10.1080/10408340600969957. arXiv:https://doi.org/10.1080/10408340600969957.
  * Kovaltsuk et al. [2018] Kovaltsuk, A., Leem, J., Kelm, S., Snowden, J., Deane, C. M., & Krawczyk, K. (2018).  Observed antibody space: a resource for data mining next-generation sequencing of antibody repertoires.  The Journal of Immunology, 201, 2502--2509.
  * Krawczyk et al. [2014] Krawczyk, K., Liu, X., Baker, T., Shi, J., & Deane, C. M. (2014).  Improving b-cell epitope prediction and its application to global antibody-antigen docking.  Bioinformatics, 30, 2288--2294.
  * Krayukhina et al. [2015] Krayukhina, E., Tsumoto, K., Uchiyama, S., & Fukui, K. (2015).  Effects of syringe material and silicone oil lubrication on the stability of pharmaceutical proteins.  Journal of pharmaceutical sciences, 104, 527--535.
  * Kringelum et al. [2012] Kringelum, J. V., Lundegaard, C., Lund, O., & Nielsen, M. (2012).  Reliable b cell epitope predictions: impacts of method development and improved benchmarking.  PLoS computational biology, 8, e1002829:1--10.
  * Kroll et al. [2017a] Kroll, P., Hofer, A., Stelzer, I. V., & Herwig, C. (2017a).  Workflow to set up substantial target-oriented mechanistic process models in bioprocess engineering.  Process Biochemistry, 62, 24--36. doi:10.1016/j.procbio.2017.07.017.
  * Kroll et al. [2017b] Kroll, P., Hofer, A., Ulonska, S., Kager, J., & Herwig, C. (2017b).  Model-based methods in the biopharmaceutical process lifecycle.  Pharmaceutical research, 34, 2596--2613.
  * Kumar et al. [2012] Kumar, S., Mitchell, M. A., Rup, B., & Singh, S. K. (2012).  Relationship between potential aggregation-prone regions and hla-dr-binding t-cell immune epitopes: implications for rational design of novel and follow-on therapeutic antibodies.  Journal of pharmaceutical sciences, 101, 2686--2701.
  * Kumar et al. [2018] Kumar, S., Plotnikov, N. V., Rouse, J. C., & Singh, S. K. (2018).  Biopharmaceutical informatics: supporting biologic drug development via molecular modelling and informatics.  Journal of Pharmacy and Pharmacology, 70, 595--608. doi:10.1111/jphp.12700.
  * Kumar et al. [2016] Kumar, S., Thangakani, A. M., Nagarajan, R., Singh, S. K., Velmurugan, D., & Gromiha, M. M. (2016).  Autoimmune responses to soluble aggregates of amyloidogenic proteins involved in neurodegenerative diseases: overlapping aggregation prone and autoimmunogenic regions.  Scientific reports, 6, 1--13.
  * Kumar et al. [2015] Kumar, V., Leweke, S., von Lieres, E., & Rathore, A. S. (2015).  Mechanistic modeling of ion-exchange process chromatography of charge variants of monoclonal antibody products.  Journal of Chromatography A, 1426, 140--153. doi:10.1016/j.chroma.2015.11.062.
  * Kuroda & Tsumoto [2020] Kuroda, D., & Tsumoto, K. (2020).  Engineering stability, viscosity, and immunogenicity of antibodies by computational design.  Journal of Pharmaceutical Sciences, 109, 1631--1651.
  * Kurumida et al. [2020] Kurumida, Y., Saito, Y., & Kameda, T. (2020).  Predicting antibody affinity changes upon mutations by combining multiple predictors.  Scientific reports, 10, 1--9.
  * Kyriakopoulos et al. [2017] Kyriakopoulos, S., Ang, K. S., Lakshmanan, M., Huang, Z., Yoon, S., Gunawan, R., & Lee, D.-Y. (2017).  Kinetic modeling of mammalian cell culture bioprocessing: The quest to advance biomanufacturing.  Biotechnology Journal, 13, 1700229. doi:10.1002/biot.201700229.
  * Lai et al. [2021a] Lai, P.-K., Fernando, A., Cloutier, T. K., Gokarn, Y., Zhang, J., Schwenger, W., Chari, R., Calero-Rubio, C., & Trout, B. L. (2021a).  Machine learning applied to determine the molecular descriptors responsible for the viscosity behavior of concentrated therapeutic antibodies.  Molecular Pharmaceutics, 18, 1167--1175.
  * Lai et al. [2021b] Lai, P.-K., Fernando, A., Cloutier, T. K., Kingsbury, J. S., Gokarn, Y., Halloran, K. T., Calero-Rubio, C., & Trout, B. L. (2021b).  Machine learning feature selection for predicting high concentration therapeutic antibody aggregation.  Journal of Pharmaceutical Sciences, 110, 1583--1591.
  * Lai et al. [2022] Lai, P.-K., Gallegos, A., Mody, N., Sathish, H. A., & Trout, B. L. (2022).  Machine learning prediction of antibody aggregation and viscosity for high concentration formulation development of protein therapeutics.  mAbs, 14. doi:10.1080/19420862.2022.2026208.
  * Lai et al. [2021c] Lai, P.-K., Swan, J. W., & Trout, B. L. (2021c).  Calculation of therapeutic antibody viscosity with coarse-grained models, hydrodynamic calculations and machine learning-based parameters.  MAbs, 13, 1907882\.
  * Lakerveld et al. [2013] Lakerveld, R., Benyahia, B., Braatz, R. D., & Barton, P. I. (2013).  Model-based design of a plant-wide control strategy for a continuous pharmaceutical plant.  AIChE Journal, 59, 3671--3685. doi:10.1002/aic.14107.
  * Lakerveld et al. [2014] Lakerveld, R., Benyahia, B., Heider, P. L., Zhang, H., Wolfe, A., Testa, C. J., Ogden, S., Hersey, D. R., Mascia, S., Evans, J. M. B., Braatz, R. D., & Barton, P. I. (2014).  The application of an automated control strategy for an integrated continuous pharmaceutical pilot plant.  Organic Process Research and Development, 19, 1088--1100. doi:10.1021/op500104d.
  * Le et al. [2012] Le, H., Kabbur, S., Pollastrini, L., Sun, Z., Mills, K., Johnson, K., Karypis, G., & Hu, W.-S. (2012).  Multivariate analysis of cell culture bioprocess data—lactate consumption as process indicator.  Journal of biotechnology, 162, 210--223.
  * Leaver-Fay et al. [2011] Leaver-Fay, A., Tyka, M., Lewis, S. M., Lange, O. F., Thompson, J., Jacak, R., Kaufman, K. W., Renfrew, P. D., Smith, C. A., Sheffler, W. et al. (2011).  Rosetta3: an object-oriented software suite for the simulation and design of macromolecules.  In Methods in enzymology (pp. 545--574).  Elsevier volume 487.
  * LeCun et al. [2015] LeCun, Y., Bengio, Y., & Hinton, G. (2015).  Deep learning.  nature, 521, 436--444.
  * Lee et al. [2004] Lee, H. L., Boccazzi, P., Gorret, N., Ram, R. J., & Sinskey, A. J. (2004).  In situ bioprocess monitoring of escherichia coli bioreactions using raman spectroscopy.  Vibrational Spectroscopy, 35, 131--137. doi:10.1016/j.vibspec.2003.12.015.
  * Lee et al. [2018] Lee, J. H., Shin, J., & Realff, M. J. (2018).  Machine learning: Overview of the recent progresses and implications for the process systems engineering field.  Computers & Chemical Engineering, 114, 111--121.
  * Lemke et al. [2015] Lemke, C., Budka, M., & Gabrys, B. (2015).  Metalearning: a survey of trends and technologies.  Artificial Intelligence Review, 44, 117--130.
  * Lemke & Gabrys [2010] Lemke, C., & Gabrys, B. (2010).  Meta-learning for time series forecasting and forecast combination.  Neurocomputing, 73, 2006--2016.
  * Li et al. [2013] Li, B., Ray, B. H., Leister, K. J., & Ryder, A. G. (2013).  Performance monitoring of a mammalian cell based bioprocess using raman spectroscopy.  Analytica Chimica Acta, 796, 84--91. doi:10.1016/j.aca.2013.07.058.
  * Li et al. [2017] Li, J., Cheng, K., Wang, S., Morstatter, F., Trevino, R. P., Tang, J., & Liu, H. (2017).  Feature selection: A data perspective.  ACM computing surveys, 50, 1--45.
  * Li et al. [2000] Li, W., Yue, H. H., Valle-Cervantes, S., & Qin, S. J. (2000).  Recursive pca for adaptive process monitoring.  Journal of process control, 10, 471--486.
  * Liang et al. [2010] Liang, S., Zheng, D., Standley, D. M., Yao, B., Zacharias, M., & Zhang, C. (2010).  Epsvr and epmeta: prediction of antigenic epitopes using support vector regression and multiple server results.  BMC bioinformatics, 11, 1--6.
  * Liberis et al. [2018] Liberis, E., Veličković, P., Sormanni, P., Vendruscolo, M., & Liò, P. (2018).  Parapred: antibody paratope prediction using convolutional and recurrent neural networks.  Bioinformatics, 34, 2944--2950.
  * Little et al. [2012] Little, R. J., D’Agostino, R., Cohen, M. L., Dickersin, K., Emerson, S. S., Farrar, J. T., Frangakis, C., Hogan, J. W., Molenberghs, G., Murphy, S. A. et al. (2012).  The prevention and treatment of missing data in clinical trials.  New England Journal of Medicine, 367, 1355--1360.
  * Liu et al. [2020] Liu, G., Zeng, H., Mueller, J., Carter, B., Wang, Z., Schilz, J., Horny, G., Birnbaum, M. E., Ewert, S., & Gifford, D. K. (2020).  Antibody complementarity determining region design using high-capacity machine learning.  Bioinformatics, 36, 2126--2133.
  * Liu & Papageorgiou [2019] Liu, S., & Papageorgiou, L. G. (2019).  Optimal antibody purification strategies using data-driven models.  Engineering, 5, 1077--1092. doi:10.1016/j.eng.2019.10.011.
  * Liu et al. [2017] Liu, Y.-J., André, S., Cristau, L. S., Lagresle, S., Hannas, Z., Calvosa, É., Devos, O., & Duponchel, L. (2017).  Multivariate statistical process control (mspc) using raman spectroscopy for in-line culture cell monitoring considering time-varying batches synchronized with correlation optimized warping (COW).  Analytica Chimica Acta, 952, 9--17. doi:10.1016/j.aca.2016.11.064.
  * Lobo et al. [2021] Lobo, S. A., Baczyk, P., Wyss, B., Widmer, J. C., Jesus, L. P., Gomes, J., Batista, A. P., Hartmann, S., & Wassmann, P. (2021).  Stability liabilities of biotherapeutic proteins: Early assessment as mitigation strategy.  Journal of Pharmaceutical and Biomedical Analysis, 192, 113650.
  * Long et al. [2022] Long, X., Ma, C., Sheng, H., Chen, L., Fei, Y., Mi, L., Han, D., & Ma, J. (2022).  Transfer learning analysis for subvisible particle flow imaging of pharmaceutical formulations.  Applied Sciences, 12, 5843.
  * López-Guajardo et al. [2021] López-Guajardo, E. A., Delgado-Licona, F., Álvarez, A. J., Nigam, K. D., Montesinos-Castellanos, A., & Morales-Menendez, R. (2021).  Process intensification 4.0: A new approach for attaining new, sustainable and circular processes enabled by machine learning.  Chemical Engineering and Processing-Process Intensification, (p. 108671).
  * Lu et al. [2016] Lu, A. E., Paulson, J. A., & Braatz, R. D. (2016).  pH and conductivity control in an integrated biomanufacturing plant.  In 2016 American Control Conference (ACC).  IEEE.  doi:10.1109/acc.2016.7525168.
  * Lu et al. [2015] Lu, A. E., Paulson, J. A., Mozdzierz, N. J., Stockdale, A., Versypt, A. N. F., Love, K. R., Love, J. C., & Braatz, R. D. (2015).  Control systems technology in the advanced manufacturing of biologic drugs.  In 2015 IEEE Conference on Control Applications (CCA) (pp. 1505--1515).  IEEE.
  * Lu et al. [2013] Lu, F., Toh, P. C., Burnett, I., Li, F., Hudson, T., Amanullah, A., & Li, J. (2013).  Automated dynamic fed-batch process and media optimization for high productivity cell culture process development.  Biotechnology and bioengineering, 110, 191--205.
  * Lu et al. [2004] Lu, N., Yang, Y., Wang, F., & Gao, F. (2004).  A stage-based monitoring method for batch processes with limited reference data.  IFAC Proceedings Volumes, 37, 787--792. doi:10.1016/s1474-6670(17)31906-7.
  * Lu et al. [2020] Lu, R.-M., Hwang, Y.-C., Liu, I.-J., Lee, C.-C., Tsai, H.-Z., Li, H.-J., & Wu, H.-C. (2020).  Development of therapeutic antibodies for the treatment of diseases.  Journal of Biomedical Science, 27, 1--30.
  * Lu et al. [2022] Lu, S., Li, Y., Ma, Q., Nan, X., & Zhang, S. (2022).  A structure-based b-cell epitope prediction model through combing local and global features.  Frontiers in immunology, 13, 890943:1--11.
  * Lu et al. [2021] Lu, S., Li, Y., Nan, X., & Zhang, S. (2021).  A sequence-based antibody paratope prediction model through combing local-global information and partner features.  In International Symposium on Bioinformatics Research and Applications (pp. 179--190).  Springer.
  * Lu et al. [2005] Lu, S., Sun, X., & Zhang, Y. (2005).  Insight into metabolism of cho cells at low glucose concentration on the basis of the determination of intracellular metabolites.  Process biochemistry, 40, 1917--1921.
  * Luttmann et al. [2012] Luttmann, R., Bracewell, D. G., Cornelissen, G., Gernaey, K. V., Glassey, J., Hass, V. C., Kaiser, C., Preusse, C., Striedner, G., & Mandenius, C.-F. (2012).  Soft sensors in bioprocessing: A status report and recommendations.  Biotechnology Journal, 7, 1040--1048. doi:10.1002/biot.201100506.
  * Löfgren et al. [2018] Löfgren, A., Andersson, N., Sellberg, A., Nilsson, B., Löfgren, M., & Wood, S. (2018).  Designing an autonomous integrated downstream sequence from a batch separation process - an industrial case study.  Biotechnology Journal, 13, 1700691. doi:10.1002/biot.201700691.
  * Ma et al. [2020] Ma, Y., Noreña-Caro, D. A., Adams, A. J., Brentzel, T. B., Romagnoli, J. A., & Benton, M. G. (2020).  Machine-learning-based simulation and fed-batch control of cyanobacterial-phycocyanin production in plectonema by artificial neural network and deep reinforcement learning.  Computers and Chemical Engineering, 142, 107016. doi:10.1016/j.compchemeng.2020.107016.
  * Madani et al. [2020] Madani, A., McCann, B., Naik, N., Keskar, N. S., Anand, N., Eguchi, R. R., Huang, P.-S., & Socher, R. (2020).  Progen: Language modeling for protein generation.  arXiv preprint arXiv:2004.03497, .
  * Manavalan et al. [2018] Manavalan, B., Govindaraj, R. G., Shin, T. H., Kim, M. O., & Lee, G. (2018).  ibce-el: a new ensemble learning framework for improved linear b-cell epitope prediction.  Frontiers in immunology, 9, 1695:1--11.
  * Mandenius & Brundin [2008] Mandenius, C.-F., & Brundin, A. (2008).  Bioprocess optimization using design-of-experiments methodology.  Biotechnology progress, 24, 1191--1203.
  * Mandenius et al. [2009] Mandenius, C.-F., Graumann, K., Schultz, T. W., Premstaller, A., Olsson, I.-M., Petiot, E., Clemens, C., & Welin, M. (2009).  Quality-by-design for biotechnology-related pharmaceuticals.  Biotechnology journal, 4, 600--609.
  * Mandenius & Gustavsson [2014] Mandenius, C.-F., & Gustavsson, R. (2014).  Mini-review: soft sensors as means for PAT in the manufacture of bio-therapeutics.  Journal of Chemical Technology and Biotechnology, 90, 215--227. doi:10.1002/jctb.4477.
  * Mante et al. [2019] Mante, J., Gangadharan, N., Sewell, D. J., Turner, R., Field, R., Oliver, S. G., Slater, N., & Dikicioglu, D. (2019).  A heuristic approach to handling missing data in biologics manufacturing databases.  Bioprocess and Biosystems Engineering, 42, 657--663. doi:10.1007/s00449-018-02059-5.
  * Mao et al. [2021] Mao, J., Akhtar, J., Zhang, X., Sun, L., Guan, S., Li, X., Chen, G., Liu, J., Jeon, H.-N., Kim, M. S. et al. (2021).  Comprehensive strategies of machine-learning-based quantitative structure-activity relationship models.  Iscience, 24, 103052\.
  * Maria [2020] Maria, G. (2020).  Model-based optimization of a fed-batch bioreactor for mAb production using a hybridoma cell culture.  Molecules, 25, 5648\. doi:10.3390/molecules25235648.
  * Marks et al. [2021] Marks, C., Hummer, A. M., Chin, M., & Deane, C. M. (2021).  Humanization of antibodies using a machine learning approach on large-scale repertoire data.  Bioinformatics, 37, 4041--4047.
  * Martin & Morris [2002] Martin, E., & Morris, A. (2002).  Enhanced bio-manufacturing through advanced multivariate statistical technologies.  Journal of biotechnology, 99, 223--235.
  * Matthews et al. [2016] Matthews, T. E., Berry, B. N., Smelko, J., Moretto, J., Moore, B., & Wiltberger, K. (2016).  Closed loop control of lactate concentration in mammalian cell culture by raman spectroscopy leads to improved cell density, viability, and biopharmaceutical protein production.  Biotechnology and Bioengineering, 113, 2416--2424. doi:10.1002/bit.26018.
  * Matthews et al. [2018] Matthews, T. E., Smelko, J. P., Berry, B., Romero-Torres, S., Hill, D., Kshirsagar, R., & Wiltberger, K. (2018).  Glucose monitoring and adaptive feeding of mammalian cell culture in the presence of strong autofluorescence by near infrared raman spectroscopy.  Biotechnology Progress, 34, 1574--1580. doi:10.1002/btpr.2711.
  * May [2020] May, M. (2020).  Strategic and technology challenges in bioprocessing.  Genetic Engineering and Biotechnology News, . URL: https://www.genengnews.com/topics/bioprocessing/strategic-and-technology-challenges-in-bioprocessing.
  * Mears et al. [2017] Mears, L., Stocks, S. M., Albaek, M. O., Sin, G., & Gernaey, K. V. (2017).  Mechanistic fermentation models for process design, monitoring, and control.  Trends in biotechnology, 35, 914--924. doi:10.1016/j.tibtech.2017.07.002.
  * Mehdizadeh et al. [2015] Mehdizadeh, H., Lauri, D., Karry, K. M., Moshgbar, M., Procopio-Melino, R., & Drapeau, D. (2015).  Generic raman-based calibration models enabling real-time monitoring of cell culture bioreactors.  Biotechnology Progress, 31, 1004--1013. doi:10.1002/btpr.2079.
  * Melnyk et al. [2021] Melnyk, I., Das, P., Chenthamarakshan, V., & Lozano, A. (2021).  Benchmarking deep generative models for diverse antibody sequence design.  arXiv preprint arXiv:2111.06801, .
  * Mercier et al. [2013] Mercier, S. M., Diepenbroek, B., Dalm, M. C., Wijffels, R. H., & Streefland, M. (2013).  Multivariate data analysis as a PAT tool for early bioprocess development data.  Journal of Biotechnology, 167, 262--270.
  * Mercier et al. [2014] Mercier, S. M., Diepenbroek, B., Wijffels, R. H., & Streefland, M. (2014).  Multivariate PAT solutions for biopharmaceutical cultivation: current progress and limitations.  Trends in Biotechnology, 32, 329--336. doi:10.1016/j.tibtech.2014.03.008.
  * Mitchell [2013] Mitchell, M. (2013).  Determining criticality-process parameters and quality attributes part i: Criticality as a continuum.  BioPharm International, 26, 38--40.
  * Moller & Portner [2017] Moller, J., & Portner, R. (2017).  Model-based design of process strategies for cell culture bioprocesses: state of the art and new perspectives.  New insights into cell culture technology, (pp. 157--172).
  * Mowbray et al. [2022] Mowbray, M., Petsagkourakis, P., Chanona, A. D. R., & Zhang, D. (2022).  Safe chance constrained reinforcement learning for batch process optimization and control.  In Computer Aided Chemical Engineering (pp. 1039--1044).  Elsevier.  doi:10.1016/b978-0-323-95879-0.50174-0.
  * Mowbray et al. [2021] Mowbray, M., Savage, T., Wu, C., Song, Z., Cho, B. A., Rio-Chanona, E. A. D., & Zhang, D. (2021).  Machine learning for biochemical engineering: A review.  Biochemical Engineering Journal, 172, 108054. doi:10.1016/j.bej.2021.108054.
  * Myung et al. [2020a] Myung, Y., Pires, D. E., & Ascher, D. B. (2020a).  mmcsm-ab: guiding rational antibody engineering through multiple point mutations.  Nucleic acids research, 48, W125--W131.
  * Myung et al. [2020b] Myung, Y., Rodrigues, C. H., Ascher, D. B., & Pires, D. E. (2020b).  mcsm-ab2: guiding rational antibody design using graph-based signatures.  Bioinformatics, 36, 1453--1459.
  * Möller et al. [2019] Möller, J., Kuchemüller, K. B., Steinmetz, T., Koopmann, K. S., & Pörtner, R. (2019).  Model-assisted design of experiments as a concept for knowledge-based bioprocess development.  Bioprocess and Biosystems Engineering, 42, 867--882. doi:10.1007/s00449-019-02089-7.
  * Nagrath et al. [2004] Nagrath, D., Messac, A., Bequette, B. W., & Cramer, S. (2004).  A hybrid model framework for the optimization of preparative chromatographic processes.  Biotechnology progress, 20, 162--178.
  * Narayanan et al. [2020] Narayanan, H., Behle, L., Luna, M. F., Sokolov, M., Guillén-Gosálbez, G., Morbidelli, M., & Butté, A. (2020).  Hybrid-EKF: Hybrid model coupled with extended kalman filter for real-time monitoring and control of mammalian cell culture.  Biotechnology and Bioengineering, 117, 2703--2714. doi:10.1002/bit.27437.
  * Narayanan et al. [2022a] Narayanan, H., Bournazou, M. N. C., Gosálbez, G. G., & Butté, A. (2022a).  Functional-hybrid modeling through automated adaptive symbolic regression for interpretable mathematical expressions.  Chemical Engineering Journal, 430, 133032. doi:10.1016/j.cej.2021.133032.
  * Narayanan et al. [2021a] Narayanan, H., Dingfelder, F., Butté, A., Lorenzen, N., Sokolov, M., & Arosio, P. (2021a).  Machine learning for biologics: Opportunities for protein engineering, developability, and formulation.  Trends in Pharmacological Sciences, 42, 151--165. doi:10.1016/j.tips.2020.12.004.
  * Narayanan et al. [2021b] Narayanan, H., Dingfelder, F., Morales, I. C., Patel, B., Heding, K. E., Bjelke, J. R., Egebjerg, T., Butté, A., Sokolov, M., Lorenzen, N., & Arosio, P. (2021b).  Design of biopharmaceutical formulations accelerated by machine learning.  Molecular Pharmaceutics, 18, 3843--3853. doi:10.1021/acs.molpharmaceut.1c00469.
  * Narayanan et al. [2021c] Narayanan, H., Luna, M., Sokolov, M., Arosio, P., Butté, A., & Morbidelli, M. (2021c).  Hybrid models based on machine learning and an increasing degree of process knowledge: Application to capture chromatographic step.  Industrial and Engineering Chemistry Research, 60, 10466--10478. doi:10.1021/acs.iecr.1c01317.
  * Narayanan et al. [2022b] Narayanan, H., Luna, M., Sokolov, M., Butté, A., & Morbidelli, M. (2022b).  Hybrid models based on machine learning and an increasing degree of process knowledge: Application to cell culture processes.  Industrial and Engineering Chemistry Research, 61, 8658--8672. doi:10.1021/acs.iecr.1c04507.
  * Narayanan et al. [2019a] Narayanan, H., Luna, M. F., Stosch, M., Bournazou, M. N. C., Polotti, G., Morbidelli, M., Butté, A., & Sokolov, M. (2019a).  Bioprocessing in the digital age: The role of process models.  Biotechnology Journal, 15, 1900172. doi:10.1002/biot.201900172.
  * Narayanan et al. [2021d] Narayanan, H., Seidler, T., Luna, M. F., Sokolov, M., Morbidelli, M., & Butté, A. (2021d).  Hybrid models for the simulation and prediction of chromatographic processes for protein capture.  Journal of Chromatography A, 1650, 462248. doi:10.1016/j.chroma.2021.462248.
  * Narayanan et al. [2019b] Narayanan, H., Sokolov, M., Butté, A., & Morbidelli, M. (2019b).  Decision tree-pls (dt-pls) algorithm for the development of process: Specific local prediction models.  Biotechnology Progress, 35. doi:10.1002/btpr.2818.
  * Narayanan et al. [2019c] Narayanan, H., Sokolov, M., Morbidelli, M., & Butté, A. (2019c).  A new generation of predictive models: The added value of hybrid models for manufacturing processes of therapeutic proteins.  Biotechnology and Bioengineering, 116, 2540--2549. doi:10.1002/bit.27097.
  * Narhi et al. [2010] Narhi, L. O., Jiang, Y., Deshpande, R., Kang, S., & Shultz, J. (2010).  Approaches to control protein aggregation during bulk production.  Aggregation of therapeutic proteins, (pp. 257--299).
  * Neubauer et al. [2013] Neubauer, P., Cruz, N., Glauche, F., Junne, S., Knepper, A., & Raven, M. (2013).  Consistent development of bioprocesses from microliter cultures to the industrial scale.  Engineering in Life Sciences, 13, 224--238. doi:10.1002/elsc.201200021.
  * Nikita et al. [2021] Nikita, S., Tiwari, A., Sonawat, D., Kodamana, H., & Rathore, A. S. (2021).  Reinforcement learning based optimization of process chromatography for continuous processing of biopharmaceuticals.  Chemical Engineering Science, 230, 116171. doi:10.1016/j.ces.2020.116171.
  * Nitta et al. [2018] Nitta, N., Sugimura, T., Isozaki, A., Mikami, H., Hiraki, K., Sakuma, S., Iino, T., Arai, F., Endo, T., Fujiwaki, Y., Fukuzawa, H., Hase, M., Hayakawa, T., Hiramatsu, K., Hoshino, Y., Inaba, M., Ito, T., Karakawa, H., Kasai, Y., Koizumi, K., Lee, S., Lei, C., Li, M., Maeno, T., Matsusaka, S., Murakami, D., Nakagawa, A., Oguchi, Y., Oikawa, M., Ota, T., Shiba, K., Shintaku, H., Shirasaki, Y., Suga, K., Suzuki, Y., Suzuki, N., Tanaka, Y., Tezuka, H., Toyokawa, C., Yalikun, Y., Yamada, M., Yamagishi, M., Yamano, T., Yasumoto, A., Yatomi, Y., Yazawa, M., Carlo, D. D., Hosokawa, Y., Uemura, S., Ozeki, Y., & Goda, K. (2018).  Intelligent image-activated cell sorting.  Cell, 175, 266--276.e13. doi:10.1016/j.cell.2018.08.028.
  * Noguchi et al. [2009] Noguchi, M., Parampalli, A., Abbott, I., Setiabudi, G., Chiruvolu, V., Blanchard, M., & Lutz, H. (2009).  Considerations for scaling-up depth filtration of harvested cell culture fluid.  BioPharm International, 22.
  * Norman et al. [2020] Norman, R. A., Ambrosetti, F., Bonvin, A. M., Colwell, L. J., Kelm, S., Kumar, S., & Krawczyk, K. (2020).  Computational approaches to therapeutic antibody design: established methods and emerging trends.  Briefings in bioinformatics, 21, 1549--1567.
  * Obrezanova et al. [2015] Obrezanova, O., Arnell, A., De La Cuesta, R. G., Berthelot, M. E., Gallagher, T. R., Zurdo, J., & Stallwood, Y. (2015).  Aggregation risk prediction for antibodies and its application to biotherapeutic development.  MAbs, 7, 352--363.
  * O’Brien et al. [2012] O’Brien, T. P., Brown, L. A., Battersby, D. G., Rudolph, A. S., & Raman, L. (2012).  Large-scale, single-use depth filtration systems for mammalian cell culture clarification.  BioProcess Int, 10, 50--57.
  * O’Connell et al. [2010] O’Connell, M.-L., Ryder, A. G., Leger, M. N., & Howley, T. (2010).  Qualitative analysis using raman spectroscopy and chemometrics: a comprehensive model system for narcotics analysis.  Applied spectroscopy, 64, 1109--1121.
  * Oh et al. [2022] Oh, T. H., Park, H. M., Kim, J. W., & Lee, J. M. (2022).  Integration of reinforcement learning and model predictive control to optimize semi-batch bioreactor.  AIChE Journal, 68. doi:10.1002/aic.17658.
  * Ohadi et al. [2014] Ohadi, K., Legge, R. L., & Budman, H. M. (2014).  Development of a soft-sensor based on multi-wavelength fluorescence spectroscopy and a dynamic metabolic model for monitoring mammalian cell cultures.  Biotechnology and Bioengineering, 112, 197--208. doi:10.1002/bit.25339.
  * Ohadi et al. [2015] Ohadi, K., Legge, R. L., & Budman, H. M. (2015).  Intrinsic fluorescence based at situ soft sensor for monitoring monoclonal antibody aggregation.  Biotechnology Progress, 31, 1423--1432. doi:10.1002/btpr.2140.
  * Olimpieri et al. [2013] Olimpieri, P. P., Chailyan, A., Tramontano, A., & Marcatili, P. (2013).  Prediction of site-specific interactions in antibody-antigen complexes: the proabc method and server.  Bioinformatics, 29, 2285--2291.
  * Paidi et al. [2016] Paidi, S. K., Siddhanta, S., Strouse, R., McGivney, J. B., Larkin, C., & Barman, I. (2016).  Rapid identification of biotherapeutics with label-free raman spectroscopy.  Analytical Chemistry, 88, 4361--4368. doi:10.1021/acs.analchem.5b04794.
  * Pais et al. [2014] Pais, D. A., Carrondo, M. J., Alves, P. M., & Teixeira, A. P. (2014).  Towards real-time monitoring of therapeutic protein quality in mammalian cell processes.  Current Opinion in Biotechnology, 30, 161--167. doi:10.1016/j.copbio.2014.06.019.
  * Pampuri et al. [2011] Pampuri, S., Schirru, A., Fazio, G., & De Nicolao, G. (2011).  Multilevel lasso applied to virtual metrology in semiconductor manufacturing.  In 2011 IEEE international conference on automation science and engineering (pp. 244--249).  IEEE.
  * Pandian & Noel [2018] Pandian, B. J., & Noel, M. M. (2018).  Control of a bioreactor using a new partially supervised reinforcement learning algorithm.  Journal of Process Control, 69, 16--29. doi:10.1016/j.jprocont.2018.07.013.
  * Papathanasiou et al. [2019] Papathanasiou, M. M., Burnak, B., Katz, J., Shah, N., & Pistikopoulos, E. N. (2019).  Assisting continuous biomanufacturing through advanced control in downstream purification.  Computers and Chemical Engineering, 125, 232--248. doi:10.1016/j.compchemeng.2019.03.013.
  * Papathanasiou & Kontoravdi [2020] Papathanasiou, M. M., & Kontoravdi, C. (2020).  Engineering challenges in therapeutic protein product and process design.  Current Opinion in Chemical Engineering, 27, 81--88. doi:10.1016/j.coche.2019.11.010.
  * Papathanasiou et al. [2017] Papathanasiou, M. M., Quiroga-Campano, A. L., Steinebach, F., Elviro, M., Mantalaris, A., & Pistikopoulos, E. N. (2017).  Advanced model-based control strategies for the intensification of upstream and downstream processing in mAb production.  Biotechnology Progress, 33, 966--988. doi:10.1002/btpr.2483.
  * Park et al. [2021] Park, S.-Y., Park, C.-H., Choi, D.-H., Hong, J. K., & Lee, D.-Y. (2021).  Bioprocess digital twins of mammalian cell culture for advanced biomanufacturing.  Current Opinion in Chemical Engineering, 33, 100702. doi:10.1016/j.coche.2021.100702.
  * Peters & Hennessey [2020] Peters, G. L., & Hennessey, E. K. (2020).  Naming of biological products.  US Pharmacist, 45, 33--36.
  * Petsagkourakis et al. [2020] Petsagkourakis, P., Sandoval, I., Bradford, E., Zhang, D., & del Rio-Chanona, E. (2020).  Reinforcement learning for batch bioprocess optimization.  Computers and Chemical Engineering, 133, 106649. doi:10.1016/j.compchemeng.2019.106649.
  * Pham et al. [2022] Pham, T. D., Manapragada, C., Sun, Y., Bassett, R., & Aickelin, U. (2022).  A scoping review of supervised learning modelling and data-driven optimisation in monoclonal antibody process development.  Submitted to Digital Chemical Engineering, .
  * Pires & Ascher [2016] Pires, D. E., & Ascher, D. B. (2016).  mcsm-ab: a web server for predicting antibody--antigen affinity changes upon mutation with graph-based signatures.  Nucleic acids research, 44, W469--W473.
  * Pirrung et al. [2017] Pirrung, S. M., van der Wielen, L. A., van Beckhoven, R. F., van de Sandt, E. J., Eppink, M. H., & Ottens, M. (2017).  Optimization of biopharmaceutical downstream processes supported by mechanistic models and artificial neural networks.  Biotechnology Progress, 33, 696--707.
  * Pittala & Bailey-Kellogg [2020] Pittala, S., & Bailey-Kellogg, C. (2020).  Learning context-aware structural representations to predict antigen and antibody binding interfaces.  Bioinformatics, 36, 3996--4003.
  * Pokhriyal et al. [2022] Pokhriyal, P., Gupta, P., Khambhampaty, S., Ullanat, R., & Pathak, M. (2022).  Recent trends in data analytics for upstream process workflows.  BioPharm International, 35, 20--25.
  * Poth et al. [2022] Poth, M., Magill, G., Filgertshofer, A., Popp, O., & Großkopf, T. (2022).  Extensive evaluation of machine learning models and data preprocessings for raman modeling in bioprocessing.  Journal of Raman Spectroscopy, . doi:10.1002/jrs.6402.
  * Powers et al. [2019] Powers, D. N., Wang, Y., Fratz-Berilla, E. J., Velugula-Yellela, S. R., Chavez, B., Angart, P., Trunfio, N., Yoon, S., & Agarabi, C. (2019).  Real-time quantification and supplementation of bioreactor amino acids to prolong culture time and maintain antibody product quality.  Biotechnology Progress, 35. doi:10.1002/btpr.2894.
  * Prihoda et al. [2022] Prihoda, D., Maamary, J., Waight, A., Juan, V., Fayadat-Dilman, L., Svozil, D., & Bitton, D. A. (2022).  Biophi: A platform for antibody design, humanization, and humanness evaluation based on natural antibody repertoires and deep learning.  MAbs, 14, e2020203:1--16.
  * Printz & Friess [2012] Printz, M., & Friess, W. (2012).  Simultaneous detection and analysis of protein aggregation and protein unfolding by size exclusion chromatography with post column addition of the fluorescent dye bisans.  Journal of pharmaceutical sciences, 101, 826--837.
  * Puranik et al. [2022] Puranik, A., Dandekar, P., & Jain, R. (2022).  Exploring the potential of machine learning for more efficient development and production of biopharmaceuticals.  Biotechnology Progress, .
  * Radhakrishnan et al. [2018] Radhakrishnan, D., Wells, E. A., & Robinson, A. S. (2018).  Strategies to enhance productivity and modify product quality in therapeutic proteins.  Current Opinion in Chemical Engineering, 22, 81--88.
  * Rafferty et al. [2019] Rafferty, C., O'Mahony, J., Burgoyne, B., Rea, R., Balss, K. M., & Latshaw, D. C. (2019).  Raman spectroscopy as a method to replace off-line pH during mammalian cell culture processes.  Biotechnology and Bioengineering, 117, 146--156. doi:10.1002/bit.27197.
  * Randek & Mandenius [2017] Randek, J., & Mandenius, C.-F. (2017).  On-line soft sensing in upstream bioprocessing.  Critical Reviews in Biotechnology, 38, 106--121. doi:10.1080/07388551.2017.1312271.
  * Rashedi et al. [2022] Rashedi, M., Khodabandehlou, H., Demers, M., Wang, T., & Garvin, C. (2022).  Model predictive controller design for bioprocesses based on machine learning algorithms.  IFAC-PapersOnLine, 55, 45--50. doi:10.1016/j.ifacol.2022.07.420.
  * Rathore & Singh [2015] Rathore, A., & Singh, S. (2015).  Use of multivariate data analysis in bioprocessing.  BioPharm International, 28, 26--31.
  * Rathore & Kapoor [2014] Rathore, A. S., & Kapoor, G. (2014).  Application of process analytical technology for downstream purification of biotherapeutics.  Journal of Chemical Technology and Biotechnology, 90, 228--236. doi:10.1002/jctb.4447.
  * Rathore et al. [2014] Rathore, A. S., Mittal, S., Pathak, M., & Mahalingam, V. (2014).  Chemometrics application in biotech processes: assessing comparability across processes and scales.  Journal of Chemical Technology & Biotechnology, 89, 1311--1316.
  * Rathore et al. [2015] Rathore, A. S., Singh, S. K., Pathak, M., Read, E. K., Brorson, K. A., Agarabi, C. D., & Khan, M. (2015).  Fermentanomics: Relating quality attributes of a monoclonal antibody to cell culture process variables and raw materials using multivariate data analysis.  Biotechnology Progress, 31, 1586--1599. doi:10.1002/btpr.2155.
  * Rathore & Winkle [2009] Rathore, A. S., & Winkle, H. (2009).  Quality by design for biopharmaceuticals.  Nature Biotechnology, 27, 26--34.
  * Rawi et al. [2018] Rawi, R., Mall, R., Kunji, K., Shen, C.-H., Kwong, P. D., & Chuang, G.-Y. (2018).  Parsnip: sequence-based protein solubility prediction using gradient boosting machine.  Bioinformatics, 34, 1092--1098.
  * Raybould et al. [2019] Raybould, M. I., Marks, C., Krawczyk, K., Taddese, B., Nowak, J., Lewis, A. P., Bujotzek, A., Shi, J., & Deane, C. M. (2019).  Five computational developability guidelines for therapeutic antibody profiling.  Proceedings of the National Academy of Sciences, 116, 4025--4030.
  * Raybould et al. [2020] Raybould, M. I., Marks, C., Lewis, A. P., Shi, J., Bujotzek, A., Taddese, B., & Deane, C. M. (2020).  Thera-sabdab: the therapeutic structural antibody database.  Nucleic acids research, 48, D383--D388.
  * Reinhart et al. [2015] Reinhart, D., Damjanovic, L., Kaisermayer, C., & Kunert, R. (2015).  Benchmarking of commercially available cho cell culture media for antibody production.  Applied microbiology and biotechnology, 99, 4645--4657.
  * van Reis & Zydney [2007] van Reis, R., & Zydney, A. (2007).  Bioprocess membrane technology.  Journal of Membrane Science, 297, 16--50.
  * Ren et al. [2017] Ren, J., Song, J., Ellis, J., & Li, J. (2017).  Staged heterogeneity learning to identify conformational b-cell epitopes from antigen sequences.  BMC genomics, 18, 1--13.
  * Repecka et al. [2021] Repecka, D., Jauniskis, V., Karpus, L., Rembeza, E., Rokaitis, I., Zrimec, J., Poviloniene, S., Laurynenas, A., Viknander, S., Abuajwa, W. et al. (2021).  Expanding functional protein sequence spaces using generative adversarial networks.  Nature Machine Intelligence, 3, 324--333.
  * Reyes et al. [2022] Reyes, S. J., Durocher, Y., Pham, P. L., & Henry, O. (2022).  Modern sensor tools and techniques for monitoring, controlling, and improving cell culture processes.  Processes, 10, 189\.
  * Richelle et al. [2020] Richelle, A., Lee, B. W., Portela, R. M. C., Raley, J., & Stosch, M. (2020).  Analysis of transformed upstream bioprocess data provides insights into biological system variation.  Biotechnology Journal, 15, 2000113. doi:10.1002/biot.202000113.
  * Ritacco et al. [2018] Ritacco, F. V., Wu, Y., & Khetan, A. (2018).  Cell culture media for recombinant protein expression in chinese hamster ovary (cho) cells: history, key components, and optimization strategies.  Biotechnology progress, 34, 1407--1426.
  * Rives et al. [2021] Rives, A., Meier, J., Sercu, T., Goyal, S., Lin, Z., Liu, J., Guo, D., Ott, M., Zitnick, C. L., Ma, J. et al. (2021).  Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences.  Proceedings of the National Academy of Sciences, 118, e2016239118.
  * Rivolli et al. [2022] Rivolli, A., Garcia, L. P., Soares, C., Vanschoren, J., & de Carvalho, A. (2022).  Meta-features for meta-learning.  Knowledge-Based Systems, 240, 108101. doi:10.1016/j.knosys.2021.108101.
  * Robert et al. [2021] Robert, P. A., Akbar, R., Frank, R., Pavlović, M., Widrich, M., Snapkov, I., Chernigovskaya, M., Scheffer, L., Slabodkin, A., Mehta, B. B. et al. (2021).  One billion synthetic 3d-antibody-antigen complexes enable unconstrained machine-learning formalized investigation of antibody specificity prediction.  BioRXiV, .
  * Roch & Mandenius [2016] Roch, P., & Mandenius, C.-F. (2016).  On-line monitoring of downstream bioprocesses.  Current Opinion in Chemical Engineering, 14, 112--120. doi:10.1016/j.coche.2016.09.007.
  * Rocklin et al. [2017] Rocklin, G. J., Chidyausiku, T. M., Goreshnik, I., Ford, A., Houliston, S., Lemak, A., Carter, L., Ravichandran, R., Mulligan, V. K., Chevalier, A. et al. (2017).  Global analysis of protein folding using massively parallel design, synthesis, and testing.  Science, 357, 168--175.
  * Rodionova & Pomerantsev [2010] Rodionova, O. Y., & Pomerantsev, A. (2010).  Nir-based approach to counterfeit-drug detection.  TrAC Trends in Analytical Chemistry, 29, 795--803.
  * Rodriguez-Granrose et al. [2021] Rodriguez-Granrose, D., Jones, A., Loftus, H., Tandeski, T., Heaton, W., Foley, K. T., & Silverman, L. (2021).  Design of experiment (DOE) applied to artificial neural network architecture enables rapid bioprocess improvement.  Bioprocess and Biosystems Engineering, 44, 1301--1308. doi:10.1007/s00449-021-02529-3.
  * Rolinger et al. [2020] Rolinger, L., Rüdt, M., & Hubbuch, J. (2020).  A critical review of recent trends, and a future perspective of optical spectroscopy as pat in biopharmaceutical downstream processing.  Analytical and Bioanalytical Chemistry, 412, 2047--2064. doi:10.1007/s00216-020-02407-z.
  * Romero et al. [2013] Romero, P. A., Krause, A., & Arnold, F. H. (2013).  Navigating the protein fitness landscape with gaussian processes.  Proceedings of the National Academy of Sciences, 110, E193--E201.
  * Romero-Torres et al. [2018] Romero-Torres, S., Wolfram, K., Armando, J., Ahmed, S., Ren, J., Shi, C., & Guenard, R. (2018).  Biopharmaceutical process model evolution-enabling process knowledge continuum from an advanced process control perspective.  American Pharmaceutical Review, 21, 1--10.
  * Rouiller et al. [2016] Rouiller, Y., Bielser, J.-M., Bruhlmann, D., Jordan, M., Broly, H., & Stettler, M. (2016).  Screening and assessment of performance and molecule quality attributes of industrial cell lines across different fed-batch systems.  Biotechnology progress, 32, 160--170.
  * Rouiller et al. [2013] Rouiller, Y., Périlleux, A., Collet, N., Jordan, M., Stettler, M., & Broly, H. (2013).  A high-throughput media design approach for high performance mammalian fed-batch cultures.  mAbs, 5, 501--511. doi:10.4161/mabs.23942.
  * Rouiller et al. [2012] Rouiller, Y., Solacroup, T., Deparis, V., Barbafieri, M., Gleixner, R., Broly, H., & Eon-Duval, A. (2012).  Application of quality by design to the characterization of the cell culture process of an fc-fusion protein.  European Journal of Pharmaceutics and Biopharmaceutics, 81, 426--437. doi:10.1016/j.ejpb.2012.02.018.
  * Rüdt et al. [2016] Rüdt, M., Brestrich, N., Rolinger, L., & Hubbuch, J. (2016).  Real-time monitoring and control of the load phase of a protein a capture step.  Biotechnology and Bioengineering, 114, 368--373. doi:10.1002/bit.26078.
  * Rüdt et al. [2017] Rüdt, M., Briskot, T., & Hubbuch, J. (2017).  Advances in downstream processing of biologics - spectroscopy: An emerging process analytical technology.  Journal of Chromatography A, 1490, 2--9. doi:10.1016/j.chroma.2016.11.010.
  * Saggu et al. [2017] Saggu, M., Patel, A. R., & Koulis, T. (2017).  A random forest approach for counting silicone oil droplets and protein particles in antibody formulations using flow microscopy.  Pharmaceutical research, 34, 479--491.
  * Sagmeister et al. [2021] Sagmeister, P., Lebl, R., Castillo, I., Rehrl, J., Kruisz, J., Sipek, M., Horn, M., Sacher, S., Cantillo, D., Williams, J. D. et al. (2021).  Advanced real-time process analytics for multistep synthesis in continuous flow.  Angewandte Chemie International Edition, 60, 8139--8148.
  * Saha & Raghava [2006] Saha, S., & Raghava, G. P. S. (2006).  Prediction of continuous b-cell epitopes in an antigen using recurrent neural network.  Proteins: Structure, Function, and Bioinformatics, 65, 40--48.
  * Sahin et al. [2010] Sahin, E., Grillo, A. O., Perkins, M. D., & Roberts, C. J. (2010).  Comparative effects of ph and ionic strength on protein--protein interactions, unfolding, and aggregation for igg1 antibodies.  Journal of pharmaceutical sciences, 99, 4830--4848.
  * Saito et al. [2018] Saito, Y., Oikawa, M., Nakazawa, H., Niide, T., Kameda, T., Tsuda, K., & Umetsu, M. (2018).  Machine-learning-guided mutagenesis for directed evolution of fluorescent proteins.  ACS synthetic biology, 7, 2014--2022.
  * Saka et al. [2021] Saka, K., Kakuzaki, T., Metsugi, S., Kashiwagi, D., Yoshida, K., Wada, M., Tsunoda, H., & Teramoto, R. (2021).  Antibody design using lstm based deep generative model from phage display library for affinity maturation.  Scientific reports, 11, 1--13.
  * Salim et al. [2022] Salim, T., Chauhan, G., Templeton, N., & Ling, W. L. W. (2022).  Using mvda with stoichiometric balances to optimize amino acid concentrations in chemically defined cho cell culture medium for improved culture performance.  Biotechnology and Bioengineering, 119, 452--469.
  * Salvador et al. [2016] Salvador, M. M., Budka, M., & Gabrys, B. (2016).  Adapting multicomponent predictive systems using hybrid adaptation strategies with auto-weka in process industry.  In Workshop on Automatic Machine Learning (pp. 48--57).  PMLR.
  * Salvador et al. [2018] Salvador, M. M., Budka, M., & Gabrys, B. (2018).  Automatic composition and optimization of multicomponent predictive systems with an extended auto-weka.  IEEE Transactions on Automation Science and Engineering, 16, 946--959.
  * Sanfeliu et al. [1996] Sanfeliu, A., Cairo, J. J., Casas, C., Sola, C., & Godia, F. (1996).  Analysis of nutritional factors and physical conditions affecting growth and monoclonal antibody production of the hybridoma kb-26.5 cell line.  Biotechnology progress, 12, 209--216.
  * Sankar et al. [2018] Sankar, K., Hoi, K. H., Yin, Y., Ramachandran, P., Andersen, N., Hilderbrand, A., McDonald, P., Spiess, C., & Zhang, Q. (2018).  Prediction of methionine oxidation risk in monoclonal antibodies using a machine learning method.  mAbs, 10, 1281--1290. doi:10.1080/19420862.2018.1518887.
  * Santos et al. [2019] Santos, R. M., Kaiser, P., Menezes, J. C., & Peinado, A. (2019).  Improving reliability of raman spectroscopy for mAb production by upstream processes during bioprocess development stages.  Talanta, 199, 396--406. doi:10.1016/j.talanta.2019.02.088.
  * Santos et al. [2018] Santos, R. M., Kessler, J.-M., Salou, P., Menezes, J. C., & Peinado, A. (2018).  Monitoring mab cultivations with in-situ raman spectroscopy: The influence of spectral selectivity on calibration models and industrial use as reliable pat tool.  Biotechnology progress, 34, 659--670.
  * Satheka [2022] Satheka, A. C. (2022).  Upscaling of clinical grade stem cell production: Upstream processing (usp) and downstream processing (dsp) operations of cell expansion, harvesting, detachment, separation, washing and concentration steps, and the regulatory requirements.  In Stem Cell Production (pp. 159--184).  Springer.
  * Schaeffer [2019] Schaeffer, B. (2019).  Bioprocess intensification: aspirations and achievements.  URL: https://boydbiomedical.com/articles/challenges-and-risks-in-batch-bioprocessing [Online; accessed Oct 30, 2022].
  * Schmidberger et al. [2015] Schmidberger, T., Posch, C., Sasse, A., Gülch, C., & Huber, R. (2015).  Progress toward forecasting product quality and quantity of mammalian cell culture processes by performance-based modeling.  Biotechnology Progress, 31, 1119--1127. doi:10.1002/btpr.2105.
  * Schneider et al. [2022] Schneider, C., Buchanan, A., Taddese, B., & Deane, C. M. (2022).  Dlab: deep learning methods for structure-based virtual screening of antibodies.  Bioinformatics, 38, 377--383.
  * Schneider et al. [2020] Schneider, P., Walters, W. P., Plowright, A. T., Sieroka, N., Listgarten, J., Goodnow, R. A., Fisher, J., Jansen, J. M., Duca, J. S., Rush, T. S. et al. (2020).  Rethinking drug design in the artificial intelligence era.  Nature Reviews Drug Discovery, 19, 353--364.
  * Schuster et al. [2021] Schuster, J., Mahler, H.-C., Joerg, S., Huwyler, J., & Mathaes, R. (2021).  Analytical challenges assessing protein aggregation and fragmentation under physiologic conditions.  Journal of Pharmaceutical Sciences, 110, 3103--3110. doi:10.1016/j.xphs.2021.04.014.
  * Scriven et al. [2022] Scriven, A., Kedziora, D. J., Musial, K., & Gabrys, B. (2022).  The technological emergence of automl: A survey of performant software and applications in the context of industry.  arXiv preprint arXiv:2211.04148, .
  * Sela-Culang et al. [2014] Sela-Culang, I., Benhnia, M. R.-E.-I., Matho, M. H., Kaever, T., Maybeno, M., Schlossman, A., Nimrod, G., Li, S., Xiang, Y., Zajonc, D. et al. (2014).  Using a combined computational-experimental approach to predict antibody-specific b cell epitopes.  Structure, 22, 646--657.
  * Selvarasu et al. [2010] Selvarasu, S., Kim, D. Y., Karimi, I. A., & Lee, D.-Y. (2010).  Combined data preprocessing and multivariate statistical analysis characterizes fed-batch culture of mouse hybridoma cells for rational medium design.  Journal of Biotechnology, 150, 94--100. doi:10.1016/j.jbiotec.2010.07.016.
  * Severson et al. [2015] Severson, K., VanAntwerp, J. G., Natarajan, V., Antoniou, C., Thömmes, J., & Braatz, R. D. (2015).  Elastic net with monte carlo sampling for data-based modeling in biopharmaceutical manufacturing facilities.  Computers and Chemical Engineering, 80, 30--36. doi:10.1016/j.compchemeng.2015.05.006.
  * Severson et al. [2017] Severson, K. A., Molaro, M. C., & Braatz, R. D. (2017).  Principal component analysis of process datasets with missing values.  Processes, 5, 38\.
  * Shah et al. [2011] Shah, R. B., Brorson, K., Read, E. K., Park, J. T., Watts, C., & Khan, M. A. (2011).  Scientific and regulatory overview of process analytical technology in bioprocesses.  In PAT Applied in Biopharmaceutical Process Development and Manufacturing: An Enabling Tool for Quality-by-Design (pp. 1--8).  CRC Press.
  * Shan et al. [2018] Shan, L., Mody, N., Sormani, P., Rosenthal, K. L., Damschroder, M. M., & Esfandiary, R. (2018).  Developability assessment of engineered monoclonal antibody variants with a complex self-association behavior using complementary analytical and in silico tools.  Molecular pharmaceutics, 15, 5697--5710.
  * Sharma et al. [2021] Sharma, A. K., Bhandari, R., Pinca-Bretotean, C., Sharma, C., Dhakad, S. K., & Mathur, A. (2021).  A study of trends and industrial prospects of industry 4.0.  Materials Today: Proceedings, 47, 2364--2369.
  * Shin et al. [2021] Shin, J.-E., Riesselman, A. J., Kollasch, A. W., McMahon, C., Simon, E., Sander, C., Manglik, A., Kruse, A. C., & Marks, D. S. (2021).  Protein design and variant prediction using autoregressive generative models.  Nature communications, 12, 1--11.
  * Shukla & Kandula [2008] Shukla, A., & Kandula, J. R. (2008).  Harvest and recovery of monoclonal antibodies from large-scale mammalian cell culture.  BioPharm International, 21.
  * Shukla et al. [2007] Shukla, A. A., Hubbard, B., Tressel, T., Guhan, S., & Low, D. (2007).  Downstream processing of monoclonal antibodies - application of platform approaches.  Journal of Chromatography B, 848, 28--39.
  * Shukla & Thommes [2010] Shukla, A. A., & Thommes, J. (2010).  Recent advances in large-scale production of monoclonal antibodies and related proteins.  Trends in Biotechnology, 28, 253--261. doi:10.1016/j.tibtech.2010.02.001.
  * Simaria et al. [2012] Simaria, A. S., Turner, R., & Farid, S. S. (2012).  A multi-level meta-heuristic algorithm for the optimisation of antibody purification processes.  Biochemical Engineering Journal, 69, 144--154. doi:10.1016/j.bej.2012.08.013.
  * Singh et al. [2015] Singh, G. P., Goh, S., Canzoneri, M., & Ram, R. J. (2015).  Raman spectroscopy of complex defined media: biopharmaceutical applications.  Journal of Raman Spectroscopy, 46, 545--550. doi:10.1002/jrs.4686.
  * Singh et al. [2017] Singh, V., Haque, S., Niwas, R., Srivastava, A., Pasupuleti, M., & Tripathi, C. (2017).  Strategies for fermentation medium optimization: an in-depth review.  Frontiers in microbiology, 7, 2087.
  * Singh et al. [2009] Singh, V., Khan, M., Khan, S., & Tripathi, C. (2009).  Optimization of actinomycin v production by streptomyces triostinicus using artificial neural network and genetic algorithm.  Applied microbiology and biotechnology, 82, 379--385.
  * Singla et al. [2016] Singla, A., Bansal, R., Joshi, V., & Rathore, A. S. (2016).  Aggregation kinetics for igg1-based monoclonal antibody therapeutics.  The AAPS journal, 18, 689--702.
  * Sivo et al. [2021] Sivo, A., de Souza Galaverna, R., Gomes, G. R., Pastre, J. C., & Vilé, G. (2021).  From circular synthesis to material manufacturing: advances, challenges, and future steps for using flow chemistry in novel application area.  Reaction Chemistry & Engineering, 6, 756--786.
  * Smiatek et al. [2020] Smiatek, J., Jung, A., & Bluhmki, E. (2020).  Towards a digital bioprocess replica: Computational approaches in biopharmaceutical development and manufacturing.  Trends in Biotechnology, 38, 1141--1153. doi:10.1016/j.tibtech.2020.05.008.
  * Sofron et al. [2016] Sofron, A., Ritz, D., Neri, D., & Fugmann, T. (2016).  High-resolution analysis of the murine mhc class ii immunopeptidome.  European journal of immunology, 46, 319--328.
  * Sokolov [2020] Sokolov, M. (2020).  Decision making and risk management in biopharmaceutical engineering—opportunities in the age of covid-19 and digitalization.  Industrial & Engineering Chemistry Research, 59, 17587--17592.
  * Sokolov et al. [2018] Sokolov, M., Morbidelli, M., Butté, A., Souquet, J., & Broly, H. (2018).  Sequential multivariate cell culture modeling at multiple scales supports systematic shaping of a monoclonal antibody toward a quality target.  Biotechnology Journal, 13, 1700461. doi:10.1002/biot.201700461.
  * Sokolov et al. [2016] Sokolov, M., Ritscher, J., MacKinnon, N., Bielser, J.-M., Brühlmann, D., Rothenhäusler, D., Thanei, G., Soos, M., Stettler, M., Souquet, J., Broly, H., Morbidelli, M., & Butté, A. (2016).  Robust factor selection in early cell culture process development for the production of a biosimilar monoclonal antibody.  Biotechnology Progress, 33, 181--191. doi:10.1002/btpr.2374.
  * Sokolov et al. [2017] Sokolov, M., Ritscher, J., MacKinnon, N., Souquet, J., Broly, H., Morbidelli, M., & Butté, A. (2017).  Enhanced process understanding and multivariate prediction of the relationship between cell culture process and monoclonal antibody quality.  Biotechnology Progress, 33, 1368--1380. doi:10.1002/btpr.2502.
  * Sokolov et al. [2015] Sokolov, M., Soos, M., Neunstoecklin, B., Morbidelli, M., Butté, A., Leardi, R., Solacroup, T., Stettler, M., & Broly, H. (2015).  Fingerprint detection and process prediction by multivariate analysis of fed-batch monoclonal antibody cell culture data.  Biotechnology Progress, 31, 1633--1644. doi:10.1002/btpr.2174.
  * Sokolov et al. [2021] Sokolov, M., von Stosch, M., Narayanan, H., Feidl, F., & Butté, A. (2021).  Hybrid modeling - a key enabler towards realizing digital twins in biopharma?  Current Opinion in Chemical Engineering, 34, 100715. doi:10.1016/j.coche.2021.100715.
  * Solle et al. [2017] Solle, D., Hitzmann, B., Herwig, C., Remelhe, M. P., Ulonska, S., Wuerth, L., Prata, A., & Steckenreiter, T. (2017).  Between the poles of data-driven and mechanistic modeling for process operation.  Chemie Ingenieur Technik, 89, 542--561. doi:10.1002/cite.201600175.
  * Sommeregger et al. [2017] Sommeregger, W., Sissolak, B., Kandra, K., von Stosch, M., Mayer, M., & Striedner, G. (2017).  Quality by control: Towards model predictive control of mammalian cell culture bioprocesses.  Biotechnology Journal, 12, 1600546. doi:10.1002/biot.201600546.
  * Soons et al. [2008] Soons, Z. I., Streefland, M., van Straten, G., & van Boxtel, A. J. (2008).  Assessment of near infrared and software sensor for biomass monitoring and control.  Chemometrics and Intelligent Laboratory Systems, 94, 166--174. doi:10.1016/j.chemolab.2008.07.009.
  * Spahn et al. [2016] Spahn, P. N., Hansen, A. H., Hansen, H. G., Arnsdorf, J., Kildegaard, H. F., & Lewis, N. E. (2016).  A markov chain model for n-linked protein glycosylation - towards a low-parameter tool for model-driven glycoengineering.  Metabolic Engineering, 33, 52--66. doi:10.1016/j.ymben.2015.10.007.
  * Steinebach et al. [2017] Steinebach, F., Ulmer, N., Wolf, M., Decker, L., Schneider, V., Wälchli, R., Karst, D., Souquet, J., & Morbidelli, M. (2017).  Design and operation of a continuous integrated monoclonal antibody production process.  Biotechnology Progress, 33, 1303--1313. doi:10.1002/btpr.2522.
  * Steinwandter et al. [2019] Steinwandter, V., Borchert, D., & Herwig, C. (2019).  Data science tools and applications on the way to pharma 4.0.  Drug Discovery Today, 24, 1795--1805. doi:10.1016/j.drudis.2019.06.005.
  * Štor et al. [2021] Štor, J., Ruckerbauer, D. E., Széliová, D., Zanghellini, J., & Borth, N. (2021).  Towards rational glyco-engineering in CHO: from data to predictive models.  Current Opinion in Biotechnology, 71, 9--17. doi:10.1016/j.copbio.2021.05.003.
  * von Stosch et al. [2016] von Stosch, M., Hamelink, J.-M., & Oliveira, R. (2016).  Hybrid modeling as a qbd/pat tool in process development: an industrial e. coli case study.  Bioprocess and biosystems engineering, 39, 773--784.
  * von Stosch et al. [2011] von Stosch, M., Oliveria, R., Peres, J., & de Azevedo, S. F. (2011).  Hybrid modeling framework for process analytical technology: Application to bordetella pertussis cultures.  Biotechnology Progress, 28, 284--291. doi:10.1002/btpr.706.
  * Streif et al. [2016] Streif, S., Kim, K.-K. K., Rumschinski, P., Kishida, M., Shen, D. E., Findeisen, R., & Braatz, R. D. (2016).  Robustness analysis, prediction, and estimation for uncertain biochemical networks: An overview.  Journal of Process Control, 42, 14--34. doi:10.1016/j.jprocont.2016.03.004.
  * Sun et al. [2009] Sun, J., Wu, D., Xu, T., Wang, X., Xu, X., Tao, L., Li, Y., & Cao, Z.-W. (2009).  Seppa: a computational server for spatial epitope prediction of protein antigens.  Nucleic acids research, 37, W612--W616.
  * Sun et al. [2021] Sun, Y.-N., Shi, C., Zhang, Q.-L., Slater, N. K., Jungbauer, A., Yao, S.-J., & Lin, D.-Q. (2021).  Comparison of protein a affinity resins for twin-column continuous capture processes: Process performance and resin characteristics.  Journal of Chromatography A, 1654, 462454.
  * Sutton & Barto [2018] Sutton, R. S., & Barto, A. G. (2018).  Reinforcement learning: An introduction.  MIT press.
  * Sweredoski & Baldi [2008] Sweredoski, M. J., & Baldi, P. (2008).  Pepito: improved discontinuous b-cell epitope prediction using multiple distance thresholds and half sphere exposure.  Bioinformatics, 24, 1459--1460.
  * Sweredoski & Baldi [2009] Sweredoski, M. J., & Baldi, P. (2009).  Cobepro: a novel system for predicting continuous b-cell epitopes.  Protein Engineering, Design & Selection, 22, 113--120.
  * Tarrant et al. [2012] Tarrant, R. D., Velez-Suberbie, M. L., Tait, A. S., Smales, C. M., & Bracewell, D. G. (2012).  Host cell protein adsorption characteristics during protein a chromatography.  Biotechnology progress, 28, 1037--1044.
  * Teixeira et al. [2007] Teixeira, A. P., Alves, C., Alves, P. M., Carrondo, M. J., & Oliveira, R. (2007).  Hybrid elementary flux analysis/nonparametric modeling: application for bioprocess control.  BMC Bioinformatics, 8. doi:10.1186/1471-2105-8-30.
  * Tescione et al. [2015] Tescione, L., Lambropoulos, J., Paranandi, M. R., Makagiansar, H., & Ryll, T. (2015).  Application of bioreactor design principles and multivariate analysis for development of cell culture scale down models.  Biotechnology and Bioengineering, 112, 84--97.
  * Thakur et al. [2019] Thakur, G., Hebbi, V., & Rathore, A. S. (2019).  An nir-based pat approach for real-time control of loading in protein a chromatography in continuous manufacturing of monoclonal antibodies.  Biotechnology and Bioengineering, 117, 673--686. doi:10.1002/bit.27236.
  * Tharmalingam et al. [2015] Tharmalingam, T., Wu, C.-H., Callahan, S., & T. Goudar, C. (2015).  A framework for real-time glycosylation monitoring (rt-gm) in mammalian cell culture.  Biotechnology and Bioengineering, 112, 1146--1154.
  * Thomassen et al. [2010] Thomassen, Y. E., van Sprang, E. N., van der Pol, L. A., & Bakker, W. A. (2010).  Multivariate data analysis on historical ipv production data for better process understanding and future improvements.  Biotechnology and Bioengineering, 107, 96--104.
  * Tiwari et al. [2018] Tiwari, A., Kateja, N., Chanana, S., & Rathore, A. S. (2018).  Use of hplc as an enabler of process analytical technology in process chromatography.  Analytical Chemistry, 90, 7824--7829. doi:10.1021/acs.analchem.8b00897.
  * Tjoa & Guan [2020] Tjoa, E., & Guan, C. (2020).  A survey on explainable artificial intelligence (xai): Toward medical xai.  IEEE transactions on neural networks and learning systems, 32, 4793--4813.
  * Tobyn et al. [2018] Tobyn, M., Ferreira, A. P., Morris, C., & Menezes, J. C. (2018).  The preeminence of multivariate data analysis as a statistical data analysis technique in pharmaceutical rd and manufacturing.  In Multivariate Analysis in the Pharmaceutical Industry (pp. 3--12).  Elsevier.  doi:10.1016/b978-0-12-811065-2.00001-1.
  * Torisu et al. [2017] Torisu, T., Maruno, T., Yoneda, S., Hamaji, Y., Honda, S., Ohkubo, T., & Uchiyama, S. (2017).  Friability testing as a new stress-stability assay for biopharmaceuticals.  Journal of Pharmaceutical Sciences, 106, 2966--2978.
  * De la Torre & Albericio [2022] De la Torre, B. G., & Albericio, F. (2022).  The pharmaceutical industry in 2021. an analysis of fda drug approvals from the perspective of molecules.  Molecules, 27, 1075\.
  * Tsopanoglou & del Val [2021] Tsopanoglou, A., & del Val, I. J. (2021).  Moving towards an era of hybrid modelling: advantages and challenges of coupling mechanistic and data-driven models for upstream pharmaceutical bioprocesses.  Current Opinion in Chemical Engineering, 32, 100691. doi:10.1016/j.coche.2021.100691.
  * Tsukamoto et al. [2014] Tsukamoto, M., Watanabe, H., Ooishi, A., & Honda, S. (2014).  Engineered protein a ligands, derived from a histidine-scanning library, facilitate the affinity purification of igg under mild acidic conditions.  Journal of Biological Engineering, 8, 1--9.
  * Tulsyan et al. [2018a] Tulsyan, A., Garvin, C., & Undey, C. (2018a).  Machine-learning for biopharmaceutical batch process monitoring with limited data.  IFAC-PapersOnLine, 51, 126--131. doi:10.1016/j.ifacol.2018.09.287.
  * Tulsyan et al. [2019a] Tulsyan, A., Garvin, C., & Undey, C. (2019a).  Industrial batch process monitoring with limited data.  Journal of Process Control, 77, 114--133. doi:10.1016/j.jprocont.2019.03.002.
  * Tulsyan et al. [2018b] Tulsyan, A., Garvin, C., & Ündey, C. (2018b).  Advances in industrial biopharmaceutical batch process monitoring: Machine-learning methods for small data problems.  Biotechnology and Bioengineering, 115, 1915--1924. doi:10.1002/bit.26605.
  * Tulsyan et al. [2021] Tulsyan, A., Khodabandehlou, H., Wang, T., Schorner, G., Coufal, M., & Undey, C. (2021).  Spectroscopic models for real-time monitoring of cell culture processes using spatiotemporal just-in-time gaussian processes.  AIChE Journal, 67. doi:10.1002/aic.17210.
  * Tulsyan et al. [2019b] Tulsyan, A., Schorner, G., Khodabandehlou, H., Wang, T., Coufal, M., & Undey, C. (2019b).  A machine-learning approach to calibrate generic raman models for real-time monitoring of cell culture processes.  Biotechnology and Bioengineering, 116, 2575--2586. doi:10.1002/bit.27100.
  * Tulsyan et al. [2019c] Tulsyan, A., Wang, T., Schorner, G., Khodabandehlou, H., Coufal, M., & Undey, C. (2019c).  Automatic real-time calibration, assessment, and maintenance of generic raman models for online monitoring of cell culture processes.  Biotechnology and Bioengineering, 117, 406--416. doi:10.1002/bit.27205.
  * Undey et al. [2002] Undey, C., Williams, B. A., & Cinar, A. (2002).  Monitoring of batch pharmarceutical fermentations: Data synchronization, landmark alignment, and real-time monitoring.  IFAC Proceedings Volumes, 35, 271--276. doi:10.3182/20020721-6-es-1901.01354.
  * Urquhart [2022] Urquhart, L. (2022).  Top companies and drugs by sales in 2021.  Nature Reviews Drug Discovery, 21, 251. URL: https://doi.org/10.1038/d41573-022-00047-9.
  * US Food & Drug Administration (2018) [FDA] US Food & Drug Administration (FDA) (2018).  What are "biologics" questions and answers.  URL: https://www.fda.gov/about-fda/center-biologics-evaluation-and-research-cber/what-are-biologics-questions-and-answers.
  * Vaessen [2021] Vaessen, E. (2021).  Solving key challenges in industrial bioprocessing.  URL: https://www.labmanager.com/big-picture/bioprocessing-overview-and-trends/solving-key-challenges-in-industrial-bioprocessing-25964.
  * Vamathevan et al. [2019] Vamathevan, J., Clark, D., Czodrowski, P., Dunham, I., Ferran, E., Lee, G., Li, B., Madabhushi, A., Shah, P., Spitzer, M. et al. (2019).  Applications of machine learning in drug discovery and development.  Nature reviews Drug discovery, 18, 463--477.
  * Vann & Sheppard [2017] Vann, L., & Sheppard, J. (2017).  Use of near-infrared spectroscopy (nirs) in the biopharmaceutical industry for real-time determination of critical process parameters and integration of advanced feedback control strategies using MIDUS control.  Journal of Industrial Microbiology and Biotechnology, 44, 1589--1603. doi:10.1007/s10295-017-1984-2.
  * Vaughan & Gabrys [2016] Vaughan, N., & Gabrys, B. (2016).  Comparing and combining time series trajectories using dynamic time warping.  Procedia Computer Science, 96, 465--474.
  * Verleysen & François [2005] Verleysen, M., & François, D. (2005).  The curse of dimensionality in data mining and time series prediction.  In International work-conference on artificial neural networks (pp. 758--770).  Springer.
  * Virtanen et al. [2017] Virtanen, T., Reinikainen, S.-P., Kögler, M., Mänttäri, M., Viitala, T., & Kallioinen, M. (2017).  Real-time fouling monitoring with raman spectroscopy.  Journal of Membrane Science, 525, 312--319. doi:10.1016/j.memsci.2016.12.005.
  * Von Stosch et al. [2014] Von Stosch, M., Oliveira, R., Peres, J., & de Azevedo, S. F. (2014).  Hybrid semi-parametric modeling in process systems engineering: Past, present and future.  Computers & Chemical Engineering, 60, 86--101.
  * Walch et al. [2019] Walch, N., Scharl, T., Felföldi, E., Sauer, D. G., Melcher, M., Leisch, F., Dürauer, A., & Jungbauer, A. (2019).  Prediction of the quantity and purity of an antibody capture process in real time.  Biotechnology Journal, 14, 1800521. doi:10.1002/biot.201800521.
  * Walsh et al. [2022] Walsh, I., Myint, M., Nguyen-Khuong, T., Ho, Y. S., Ng, S. K., & Lakshmanan, M. (2022).  Harnessing the potential of machine learning for advancing - quality by design in biomanufacturing.  mAbs, 14, e2013593--1:11. doi:10.1080/19420862.2021.2013593.
  * Wang et al. [2017a] Wang, G., Briskot, T., Hahn, T., Baumann, P., & Hubbuch, J. (2017a).  Root cause investigation of deviations in protein chromatography based on mechanistic models and artificial neural networks.  Journal of Chromatography A, 1515, 146--153. doi:10.1016/j.chroma.2017.07.089.
  * Wang et al. [2017b] Wang, S., Wu, G., Zhang, X., Tian, Z., Zhang, N., Hu, T., Dai, W., & Qian, F. (2017b).  Stabilizing two igg1 monoclonal antibodies by surfactants: balance between aggregation prevention and structure perturbation.  European Journal of Pharmaceutics and Biopharmaceutics, 114, 263--277.
  * Wang et al. [2013] Wang, T., Kumru, O. S., Yi, L., Wang, Y. J., Zhang, J., Kim, J. H., Joshi, S. B., Middaugh, C. R., & Volkin, D. B. (2013).  Effect of ionic strength and ph on the physical and chemical stability of a monoclonal antibody antigen-binding fragment.  Journal of Pharmaceutical Sciences, 102, 2520--2537.
  * Wang et al. [2015] Wang, T., Wu, M.-B., Lin, J.-P., & Yang, L.-R. (2015).  Quantitative structure--activity relationship: promising advances in drug discovery platforms.  Expert opinion on drug discovery, 10, 1283--1300.
  * Wang & Georgakis [2013] Wang, Z., & Georgakis, C. (2013).  On the performance of dode in a class of in silico fermentation processes and the impact of the input domain.  IFAC Proceedings Volumes, 46, 163--168.
  * Wang & Georgakis [2017] Wang, Z., & Georgakis, C. (2017).  An in silico evaluation of data-driven optimization of biopharmaceutical processes.  AIChE Journal, 63, 2796--2805.
  * Warikoo et al. [2012] Warikoo, V., Godawat, R., Brower, K., Jain, S., Cummings, D., Simons, E., Johnson, T., Walther, J., Yu, M., Wright, B., McLarty, J., Karey, K. P., Hwang, C., Zhou, W., Riske, F., & Konstantinov, K. (2012).  Integrated continuous production of recombinant therapeutic proteins.  Biotechnology and Bioengineering, 109, 3018--3029. doi:10.1002/bit.24584.
  * Wasalathanthri et al. [2020a] Wasalathanthri, D., Ding, J., & Li, Z. (2020a).  Real time process monitoring in biologics development.  American Pharmaceutical Review, 23, 72--75.
  * Wasalathanthri et al. [2020b] Wasalathanthri, D. P., Rehmann, M. S., Song, Y., Gu, Y., Mi, L., Shao, C., Chemmalil, L., Lee, J., Ghose, S., Borys, M. C. et al. (2020b).  Technology outlook for real-time quality attribute and process parameter monitoring in biopharmaceutical development—a review.  Biotechnology and Bioengineering, 117, 3182--3198.
  * Webster et al. [2018] Webster, T. A., Hadley, B. C., Hilliard, W., Jaques, C., & Mason, C. (2018).  Development of generic raman models for a gs-kotm cho platform process.  Biotechnology Progress, 34, 730--737.
  * Wei et al. [2021] Wei, B., Woon, N., Dai, L., Fish, R., Tai, M., Handagama, W., Yin, A., Sun, J., Maier, A., McDaniel, D., Kadaub, E., Yang, J., Saggu, M., Woys, A., Pester, O., Lambert, D., Pell, A., Hao, Z., Magill, G., Yim, J., Chan, J., Yang, L., Macchi, F., Bell, C., Deperalta, G., & Chen, Y. (2021).  Multi-attribute raman spectroscopy (mars) for monitoring product quality attributes in formulated monoclonal antibody therapeutics.  mAbs, 14, e2007564. doi:10.1080/19420862.2021.2007564.
  * Weiss et al. [2016] Weiss, K., Khoshgoftaar, T. M., & Wang, D. (2016).  A survey of transfer learning.  Journal of Big data, 3, 1--40.
  * Whelan et al. [2012] Whelan, J., Craven, S., & Glennon, B. (2012).  In situ raman spectroscopy for simultaneous monitoring of multiple process parameters in mammalian cell culture bioreactors.  Biotechnology Progress, 28, 1355--1362. doi:10.1002/btpr.1590.
  * Whitaker et al. [2017] Whitaker, N., Xiong, J., Pace, S. E., Kumar, V., Middaugh, C. R., Joshi, S. B., & Volkin, D. B. (2017).  A formulation development approach to identify and select stable ultra--high-concentration monoclonal antibody formulations with reduced viscosities.  Journal of pharmaceutical sciences, 106, 3230--3241.
  * Whitford [2020] Whitford, B. (2020).  Bioprocess intensification: aspirations and achievements.  Biotechniques, 69, 84--87.
  * Wollacott et al. [2019] Wollacott, A. M., Xue, C., Qin, Q., Hua, J., Bohnuud, T., Viswanathan, K., & Kolachalama, V. B. (2019).  Quantifying the nativeness of antibody sequences using long short-term memory networks.  Protein Engineering, Design and Selection, 32, 347--354.
  * Wu et al. [2019] Wu, Z., Kan, S. J., Lewis, R. D., Wittmann, B. J., & Arnold, F. H. (2019).  Machine learning-assisted directed protein evolution with combinatorial libraries.  Proceedings of the National Academy of Sciences, 116, 8852--8858.
  * Wurm [2004] Wurm, F. M. (2004).  Production of recombinant protein therapeutics in cultivated mammalian cells.  Nature Biotechnology, 22, 1393--1398.
  * Xenopoulos [2015] Xenopoulos, A. (2015).  A new, integrated, continuous purification process template for monoclonal antibodies: Process modeling and cost of goods studies.  Journal of Biotechnology, 213, 42--53. doi:10.1016/j.jbiotec.2015.04.020.
  * Xing et al. [2010] Xing, Z., Bishop, N., Leister, K., & Li, Z. J. (2010).  Modeling kinetics of a large-scale fed-batch CHO cell culture by markov chain monte carlo method.  Biotechnology Progress, (pp. NA--NA). doi:10.1002/btpr.284.
  * Xing et al. [2009] Xing, Z., Kenty, B. M., Li, Z. J., & Lee, S. S. (2009).  Scale-up analysis for a cho cell culture process in large-scale bioreactors.  Biotechnology and bioengineering, 103, 733--746.
  * Xu et al. [2014] Xu, J., Yan, F.-r., Li, Z.-h., Wang, D., Sheng, H.-l., & Liu, Y. (2014).  Serum-free medium optimization based on trial design and support vector regression.  BioMed Research International, 2014.
  * Xu et al. [2020] Xu, Y., Verma, D., Sheridan, R. P., Liaw, A., Ma, J., Marshall, N. M., McIntosh, J., Sherer, E. C., Svetnik, V., & Johnston, J. M. (2020).  Deep dive into machine learning models for protein engineering.  Journal of chemical information and modeling, 60, 2773--2790.
  * Xu et al. [2019] Xu, Y., Wang, D., Mason, B., Rossomando, T., Li, N., Liu, D., Cheung, J. K., Xu, W., Raghava, S., Katiyar, A. et al. (2019).  Structure, heterogeneity and developability assessment of therapeutic antibodies.  MAbs, 11, 239--264.
  * Yang et al. [2019] Yang, K. K., Wu, Z., & Arnold, F. H. (2019).  Machine-learning-guided directed evolution for protein engineering.  Nature methods, 16, 687--694.
  * Yang et al. [2017] Yang, R., Jain, T., Lynaugh, H., Nobrega, R. P., Lu, X., Boland, T., Burnina, I., Sun, T., Caffry, I., Brown, M. et al. (2017).  Rapid assessment of oxidation via middle-down lcms correlates with methionine side-chain solvent-accessible surface area for 121 clinical stage monoclonal antibodies.  MAbs, 9, 646--653.
  * Yang et al. [2007] Yang, T., Breneman, C. M., & Cramer, S. M. (2007).  Investigation of multi-modal high-salt binding ion-exchange chromatography using quantitative structure–property relationship modeling.  Journal of Chromatography A, 1175, 96--105. doi:10.1016/j.chroma.2007.10.037.
  * Yang et al. [2016] Yang, Y., Niroula, A., Shen, B., & Vihinen, M. (2016).  Pon-sol: prediction of effects of amino acid substitutions on protein solubility.  Bioinformatics, 32, 2032--2034.
  * Yilmaz et al. [2020] Yilmaz, D., Mehdizadeh, H., Navarro, D., Shehzad, A., O'Connor, M., & McCormick, P. (2020).  Application of raman spectroscopy in monoclonal antibody producing continuous systems for downstream process intensification.  Biotechnology Progress, 36. doi:10.1002/btpr.2947.
  * Zalai et al. [2015] Zalai, D., Koczka, K., Párta, L., Wechselberger, P., Klein, T., & Herwig, C. (2015).  Combining mechanistic and data-driven approaches to gain process knowledge on the control of the metabolic shift to lactate uptake in a fed-batch CHO process.  Biotechnology Progress, 31, 1657--1668. doi:10.1002/btpr.2179.
  * Zhang et al. [2015] Zhang, A., Tsang, V. L., Moore, B., Shen, V., Huang, Y.-M., Kshirsagar, R., & Ryll, T. (2015).  Advanced process monitoring and feedback control to enhance cell culture process production and robustness.  Biotechnology and Bioengineering, 112, 2495--2504. URL: https://onlinelibrary.wiley.com/doi/abs/10.1002/bit.25684. doi:https://doi.org/10.1002/bit.25684. arXiv:https://onlinelibrary.wiley.com/doi/pdf/10.1002/bit.25684.
  * Zhang et al. [2019] Zhang, C., Springall, J. S., Wang, X., & Barman, I. (2019).  Rapid, quantitative determination of aggregation and particle formation for antibody drug conjugate therapeutics with label-free raman spectroscopy.  Analytica Chimica Acta, 1081, 138--145. doi:10.1016/j.aca.2019.07.007.
  * Zhang & Mao [2017] Zhang, L., & Mao, S. (2017).  Application of quality by design in the current drug development.  Asian Journal of Pharmaceutical Sciences, 12, 1--8.
  * Zhang et al. [2017] Zhang, L., Tan, J., Han, D., & Zhu, H. (2017).  From machine learning to deep learning: progress in machine intelligence for rational drug discovery.  Drug discovery today, 22, 1680--1685.
  * Zhao [2014] Zhao, C. (2014).  Phase analysis and statistical modeling with limited batches for multimode and multiphase process monitoring.  Journal of Process Control, 24, 856--870. doi:10.1016/j.jprocont.2014.04.001.
  * Zhou et al. [2015] Zhou, C., Qi, W., Lewis, E. N., & Carpenter, J. F. (2015).  Concomitant raman spectroscopy and dynamic light scattering for characterization of therapeutic proteins at high concentrations.  Analytical Biochemistry, 472, 7--20. doi:10.1016/j.ab.2014.11.016.
  * Zhu et al. [2022] Zhu, G., Xu, Z., Guo, X., Yuan, C., & Huang, Y. (2022).  Difer: Differentiable automated feature engineering.  In Proceedings of the first International Conference on Automated Machine Learning (pp. 1--17).
  * Zliobaite & Gabrys [2012] Zliobaite, I., & Gabrys, B. (2012).  Adaptive preprocessing for streaming data.  IEEE transactions on knowledge and data Engineering, 26, 309--321.
  * Zolls et al. [2013] Zolls, S., Weinbuch, D., Wiggenhorn, M., Winter, G., Friess, W., Jiskoot, W., & Hawe, A. (2013).  Flow imaging microscopy for protein particle analysis—a comparative evaluation of four different analytical instruments.  The AAPS journal, 15, 1200--1211.
  * Zou et al. [2020] Zou, M., Zhou, Z.-W., Fan, L., Zhang, W.-J., Zhao, L., Liu, X.-P., Wang, H.-B., & Tan, W.-S. (2020).  A novel method based on nonparametric regression with a gaussian kernel algorithm identifies the critical components in CHO media and feed optimization.  Journal of Industrial Microbiology and Biotechnology, 47, 63--72. doi:10.1007/s10295-019-02248-5.
  * Zupke et al. [2015] Zupke, C., Brady, L. J., Slade, P. G., Clark, P., Caspary, R. G., Livingston, B., Taylor, L., Bigham, K., Morris, A. E., & Bailey, R. W. (2015).  Real-time product attribute control to manufacture antibodies with defined n-linked glycan levels.  Biotechnology Progress, 31, 1433--1441. doi:10.1002/btpr.2136.
  * Zurdo [2013] Zurdo, J. (2013).  Developability assessment as an early de-risking tool for biopharmaceutical development.  Pharmaceutical Bioprocessing, 1, 29--50.
  * Zürcher et al. [2020] Zürcher, P., Sokolov, M., Brühlmann, D., Ducommun, R., Stettler, M., Souquet, J., Jordan, M., Broly, H., Morbidelli, M., & Butté, A. (2020).  Cell culture process metabolomics together with multivariate data analysis tools opens new routes for bioprocess development and glycosylation prediction.  Biotechnology Progress, 36. doi:10.1002/btpr.3012.