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We applied this approach to published growth-rate data of the diatom Thalassiosira pseudonana, originally measured across 25 phosphate\u2013temperature conditions. Using the nutrient\u2013temperature model as a simulator, our ML framework located the optimal growth conditions in only 25 virtual experiments\u2014matching the original study\u2019s outcome. Sensitivity analyses further revealed that fewer iterations and controlled batch sizes maintain accuracy even with higher data variability. This demonstrates that ML-guided experimentation can achieve expert-level decision-making without extensive prior data, reducing experimental burden while preserving rigour. Our results highlight the promise of algorithm-assisted experimentation in biology, agriculture, and medicine, marking a shift toward smarter, data-driven scientific workflows.<\/jats:p>","DOI":"10.3390\/make7030060","type":"journal-article","created":{"date-parts":[[2025,6,26]],"date-time":"2025-06-26T05:53:13Z","timestamp":1750917193000},"page":"60","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["A Simple Yet Powerful Hybrid Machine Learning Approach to Aid Decision-Making in Laboratory Experiments"],"prefix":"10.3390","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8800-8864","authenticated-orcid":false,"given":"Bernardo","family":"Campos Diocaretz","sequence":"first","affiliation":[{"name":"Artificial Intelligence and Cyber Futures Institute, Charles Sturt University, Bathurst, NSW 2795, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"\u00c1gota","family":"T\u0171zesi","sequence":"additional","affiliation":[{"name":"School of Medical Sciences, Faculty of Medicine and Health Sciences, The University of Sydney, Camperdown, NSW 2050, Australia"},{"name":"Translational Genomics, Garvan Institute of Medical Research, The Kinghorn Cancer Centre, Darlinghurst, NSW 2010, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2143-0213","authenticated-orcid":false,"given":"Andrei","family":"Herdean","sequence":"additional","affiliation":[{"name":"Climate Change Cluster, Faculty of Science, University of Technology Sydney, Sydney, NSW 2007, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,6,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Miller, D.M., and Jalobeanu, D. (2022). Experimental Natural History. The Cambridge History of Philosophy of the Scientific Revolution, Cambridge University Press.","DOI":"10.1017\/9781108333108"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"731","DOI":"10.1086\/597767","article-title":"\u201cThe Violence of Impediments\u201d: Francis Bacon and the Origins of Experimentation","volume":"99","author":"Merchant","year":"2008","journal-title":"Isis"},{"key":"ref_3","first-page":"104","article-title":"A new form of knowledge: Experientia Literata","volume":"5","author":"Georgescu","year":"2011","journal-title":"Soc. Politica"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1162\/posc_a_00478","article-title":"Experiment, Community, and the Constitution of Nature in the Seventeenth Century","volume":"3","author":"Garber","year":"1995","journal-title":"Perspect. Sci."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"80","DOI":"10.2307\/2857174","article-title":"Experience and Experiment: A Comparison of Zabarella\u2019s View with Galileo\u2019s in De Motu*","volume":"16","author":"Schmitt","year":"1969","journal-title":"Stud. Renaiss."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Anstey, P.R., and Schuster, J.A. (2005). The Onset of the Scientific Revolution. The Science of Nature in the Seventeenth Century: Patterns of Change in Early Modern Natural Philosophy, Springer.","DOI":"10.1007\/1-4020-3703-1"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"8","DOI":"10.62227\/as\/7302","article-title":"Unveiling the Diversity of Enlightenment Experimentation: Insights from Charles Bonnet\u2019s Naturalist Practices","volume":"73","author":"James","year":"2023","journal-title":"Arch. Sci."},{"key":"ref_8","first-page":"1","article-title":"Rise in higher education researchers and academic publications","volume":"1","author":"To","year":"2020","journal-title":"Emerald Open Res."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"38","DOI":"10.23987\/sts.55158","article-title":"What Internet Use Does and Does Not Change in Scientific Communities","volume":"16","year":"2003","journal-title":"Sci. Technol. Stud."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Buneman, P. (2005, January 11\u201313). What the Web Has Done for Scientific Data\u2014And What It Hasn\u2019t. Proceedings of the Advances in Web\u2014Age Information Management (WAIM), Hangzhou, China.","DOI":"10.1007\/11563952_1"},{"key":"ref_11","unstructured":"Trench, B. (2008). Internet: Turning science communication inside-out?. Handbook of Public Communication of Science and Technology, Routledge."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"416","DOI":"10.32628\/CSEIT241051012","article-title":"AI in Scientific Research: Empowering Researchers with Intelligent Tools","volume":"10","author":"Padakanti","year":"2024","journal-title":"Int. J. Sci. Res. Comput. Sci. Eng. Inf. Technol."},{"key":"ref_13","first-page":"100179","article-title":"Artificial intelligence: A powerful paradigm for scientific research","volume":"2","author":"Xu","year":"2021","journal-title":"Innovation"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"871","DOI":"10.1126\/science.abj8754","article-title":"Accurate prediction of protein structures and interactions using a three-track neural network","volume":"373","author":"Baek","year":"2021","journal-title":"Science"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"583","DOI":"10.1038\/s41586-021-03819-2","article-title":"Highly accurate protein structure prediction with AlphaFold","volume":"596","author":"Jumper","year":"2021","journal-title":"Nature"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"455","DOI":"10.1023\/A:1008306431147","article-title":"Efficient Global Optimization of Expensive Black-Box Functions","volume":"13","author":"Jones","year":"1998","journal-title":"J. Glob. Optim."},{"key":"ref_17","unstructured":"Montgomery, D.C. (2017). Design and Analysis of Experiments, John Wiley & Sons."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Williams, C.K., and Rasmussen, C.E. (2006). Gaussian Processes for Machine Learning, MIT Press.","DOI":"10.7551\/mitpress\/3206.001.0001"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/j.nbt.2024.12.003","article-title":"Is human oversight to AI systems still possible?","volume":"85","author":"Holzinger","year":"2025","journal-title":"New Biotechnol."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Sverchkov, Y., and Craven, M. (2017). A review of active learning approaches to experimental design for uncovering biological networks. PLoS Comput. Biol., 13.","DOI":"10.1371\/journal.pcbi.1005466"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Tharwat, A., and Schenck, W. (2023). A Survey on Active Learning: State-of-the-Art, Practical Challenges and Research Directions. Mathematics, 11.","DOI":"10.3390\/math11040820"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"623","DOI":"10.1038\/s41586-025-08710-y","article-title":"Towards multimodal foundation models in molecular cell biology","volume":"640","author":"Cui","year":"2025","journal-title":"Nature"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1080\/00401706.2021.2008505","article-title":"Active Learning for Deep Gaussian Process Surrogates","volume":"65","author":"Sauer","year":"2023","journal-title":"Technometrics"},{"key":"ref_24","unstructured":"Arthur, G., and Christian, C.R. (2016, January 9\u201311). Deep Kernel Learning. Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, Cadiz, Spain."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1109\/JPROC.2015.2494218","article-title":"Taking the Human Out of the Loop: A Review of Bayesian Optimization","volume":"104","author":"Shahriari","year":"2016","journal-title":"Proc. IEEE"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1145\/3582078","article-title":"Recent Advances in Bayesian Optimization","volume":"55","author":"Wang","year":"2023","journal-title":"ACM Comput. Surv."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Draper, N.R., and Smith, H. (1998). Applied Regression Analysis, John Wiley & Sons.","DOI":"10.1002\/9781118625590"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"James, G., Witten, D., Hastie, T., and Tibshirani, R. (2013). An Introduction to Statistical Learning, Springer.","DOI":"10.1007\/978-1-4614-7138-7"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"3269","DOI":"10.1111\/gcb.13641","article-title":"Temperature\u2013nutrient interactions exacerbate sensitivity to warming in phytoplankton","volume":"23","author":"Thomas","year":"2017","journal-title":"Glob. Change Biol."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1016\/0266-8920(92)90015-A","article-title":"An efficient sampling scheme: Updated Latin Hypercube Sampling","volume":"7","author":"Florian","year":"1992","journal-title":"Probabilistic Eng. Mech."},{"key":"ref_31","first-page":"2546","article-title":"Algorithms for hyper-parameter optimization","volume":"24","author":"Bergstra","year":"2011","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Yang, C.-T., Kristiani, E., Leong, Y.K., and Chang, J.-S. (2024). Machine learning in microalgae biotechnology for sustainable biofuel production: Advancements, applications, and prospects. Bioresour. Technol., 413.","DOI":"10.1016\/j.biortech.2024.131549"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Lapierre, F.M., Mattaliano, P., Raith, D., Castillo-Cota, M., Bermeitinger, J., and Huber, R. (2024). Multi-cycle high-throughput growth media optimization using batch Bayesian optimization. J. Chem. Technol. Biotechnol.","DOI":"10.1002\/jctb.7860"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Herdean, A., Hoch, L., Willis, A., Benediktyova, Z., Zunt, R., Trtilek, M., Trtilek, J., and Ralph, P.J. (2025). Automated phenotyping of microalgae: Scalable solution for high-throughput analysis. Bioresour. Technol., 434.","DOI":"10.1016\/j.biortech.2025.132763"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Sreeram, M., and Nof, S.Y. (2021). Human-in-the-loop: Role in cyber physical agricultural systems. Int. J. Comput. Commun. Control, 16.","DOI":"10.15837\/ijccc.2021.2.4166"},{"key":"ref_36","unstructured":"Lu, C., Lu, C., Lange, R.T., Foerster, J., Clune, J., and Ha, D. (2024). The ai scientist: Towards fully automated open-ended scientific discovery. arXiv."},{"key":"ref_37","unstructured":"Gottweis, J., Weng, W.-H., Daryin, A., Tu, T., Palepu, A., Sirkovic, P., Myaskovsky, A., Weissenberger, F., Rong, K., and Tanno, R. (2025). Towards an AI co-scientist. arXiv."}],"container-title":["Machine Learning and Knowledge Extraction"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2504-4990\/7\/3\/60\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:58:46Z","timestamp":1760032726000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2504-4990\/7\/3\/60"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,25]]},"references-count":37,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2025,9]]}},"alternative-id":["make7030060"],"URL":"https:\/\/doi.org\/10.3390\/make7030060","relation":{},"ISSN":["2504-4990"],"issn-type":[{"value":"2504-4990","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,6,25]]}}}