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The study presents a hybrid machine-learning framework that integrates Ordinary Least Squares for global surface estimation, Gaussian Process regression for uncertainty modeling, expected improvement for active learning, and K-means clustering to diversify conditions. Applied to published diatom growth-rate data across 25 phosphate–temperature settings, the method identifies optimal conditions using only 25 virtual experiments, reproducing the original outcome. Sensitivity analyses show that fewer iterations and controlled batch sizes preserve accuracy under higher variability, indicating ML-guided experimentation can support expert-level decisions with reduced experimental burden.","Article  \nA Simple Yet Powerful Hybrid Machine Learning Approach to Aid Decision-Making in Laboratory Experiments  \nBernardo Campos Diocaretz 1, Ágota T ˝uzesi 2,3 and Andrei Herdean 4, *  \nAcademic Editor: Isaac Triguero  \nReceived: 17 May 2025  \nRevised: 4 June 2025  \nAccepted: 23 June 2025  \nPublished: 25 June 2025  \nCitation: Campos Diocaretz, B.; T ˝uzesi, Á .; Herdean, A. A Simple Yet Powerful Hybrid Machine Learning Approach to Aid Decision-Making in Laboratory Experiments. Mach. Learn. Knowl. Extr. 2025, 7, 60. [https://](https://)[ ](https://)[doi.org/10.3390/make7030060](doi.org/10.3390/make7030060)  \n[Copyright:](Copyright:) © 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://creativecommons.org/](https://creativecommons.org/)[ ](https://creativecommons.org/)[licenses/by/4.0/](licenses/by/4.0/)) .  \n1 Artificial Intelligence and Cyber Futures Institute, Charles Sturt University, Bathurst, NSW 2795, Australia; [bcamposdiocaretz@csu.edu.au](bcamposdiocaretz@csu.edu.au)  \n2 School of Medical Sciences, Faculty of Medicine and Health Sciences, The University of Sydney, Camperdown, NSW 2050, Australia; [a.tuzesi@garvan.org.au](a.tuzesi@garvan.org.au)  \n3 Translational Genomics, Garvan Institute of Medical Research, The Kinghorn Cancer Centre, Darlinghurst, NSW 2010, Australia  \n4 Climate Change Cluster, Faculty of Science, University of Technology Sydney, Sydney, NSW 2007, Australia  \n* Correspondence: [andrei.herdean@uts.edu.au](andrei.herdean@uts.edu.au)  \nAbstract  \nHigh-dimensional experimental spaces and resource constraints challenge modern science. We introduce a hybrid machine-learning (ML) framework that combines Ordinary Least Squares (OLS) for global surface estimation, Gaussian Process (GP) regression for uncertainty modelling, expected improvement (EI) for active learning, and K-means clustering for diversifying conditions. We applied this approach to published growth-rate data of the diatom Thalassiosira pseudonana, originally measured across 25 phosphate–temperature conditions. Using the nutrient–temperature model as a simulator, our ML framework located the optimal growth conditions in only 25 virtual experiments—matching the original study’s 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.  \nKeywords: diatom; machine learning; Bayesian optimization  \n1. Introduction  \nScientific knowledge, as we understand it today, is the result of centuries of methodological refinement, evolving from philosophical speculation into systematic experimentation. The transformation of observational inquiry into controlled scientific experimentation during the Renaissance and Enlightenment periods was one of the most important milestones for the development of the scientific method and was marked by several key methodological innovations. Francis Bacon’s concept of “experimental natural history” introduced a new philosophy of experimentation and practice-based classification systems [1] . Bacon’s approach involved constraining nature through “the violence of impediments” [2] and emphasized the productive role of experiments in generating new effects and conceptual innovations [3] . The period saw a shift from individual observations to socially established experimental facts [4], and a growing emphasis on experience and the experimental method [5] . This era also witnessed the emergence of the mathematization of nature, corpuscularian n","cbCaiuI9i8T9MqJs","https://ap.wps.com/l/cbCaiuI9i8T9MqJs","pdf",1741184,1,13,"English","en",105,"# Introduction\n## Motivation and background: modern science and AI integration\n# Related work and key concepts\n## Hybrid ML for surrogate modeling, uncertainty, and exploration\n# Methodology\n## OLS global surface estimation\n## Gaussian Process uncertainty modeling\n## Expected Improvement active learning\n## K-means diversification of conditions\n# Experimental setup and case study\n## Diatom Thalassiosira pseudonana growth-rate data\n## Virtual experiments using nutrient–temperature simulator\n# Results\n## Optimal condition identification with limited virtual trials\n## Sensitivity analyses and batch-size/iteration effects\n# Discussion and implications","[{\"question\":\"What hybrid machine-learning components does the framework combine for laboratory decision-making?\",\"answer\":\"It combines OLS for global surface estimation, Gaussian Process regression for uncertainty modeling, expected improvement for active learning, and K-means clustering to diversify tested conditions.\"},{\"question\":\"How many virtual experiments were needed to locate the optimal diatom growth conditions?\",\"answer\":\"The approach found the optimal conditions using only 25 virtual experiments, matching the outcome of the original study measured across 25 phosphate–temperature settings.\"},{\"question\":\"Why do sensitivity analyses matter in this framework?\",\"answer\":\"They show that fewer iterations and controlled batch sizes maintain accuracy even when data variability increases, reducing experimental burden without sacrificing rigor.\"}]","A Simple Yet Powerful Hybrid Machine Learning Approach to Aid Decision-Making in Laboratory Experiments | 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